Title: Time Series Prediction with Integrated Tuning
Version: 1.2.767
Description: Time series prediction is a critical task in data analysis, requiring not only the selection of appropriate models, but also suitable data preprocessing and tuning strategies. TSPredIT (Time Series Prediction with Integrated Tuning) is a framework that provides a seamless integration of data preprocessing, decomposition, model training, hyperparameter optimization, and evaluation. Unlike other frameworks, TSPredIT emphasizes the co-optimization of both preprocessing and modeling steps, improving predictive performance. It supports a variety of statistical and machine learning models, filtering techniques, outlier detection, data augmentation, and ensemble strategies. More information is available in Salles et al. <doi:10.1007/978-3-662-68014-8_2>.
License: MIT + file LICENSE
URL: https://cefet-rj-dal.github.io/tspredit/, https://github.com/cefet-rj-dal/tspredit
BugReports: https://github.com/cefet-rj-dal/tspredit/issues
Encoding: UTF-8
RoxygenNote: 7.3.3
Depends: R (≥ 4.1.0)
Imports: stats, DescTools, e1071, elmNNRcpp, FNN, forecast, hht, KFAS, mFilter, nnet, randomForest, wavelets, dplyr, daltoolbox
NeedsCompilation: no
Packaged: 2026-02-11 06:45:03 UTC; gpca
Author: Eduardo Ogasawara ORCID iD [aut, ths, cre], Cristiane Gea [aut], Diego Carvalho [ctb], Diogo Santos [aut], Eduardo Bezerra [ctb], Esther Pacitti [ctb], Fabio Porto [ctb], Fernando Alexandrino [aut], Rebecca Salles [aut], Vitoria Birindiba [aut], CEFET/RJ [cph]
Maintainer: Eduardo Ogasawara <eogasawara@ieee.org>
Repository: CRAN
Date/Publication: 2026-02-11 08:00:02 UTC

CATS Time Series Competition

Description

Univariate time series from the CATS (Competition on Artificial Time Series) benchmark. Data Type: Artificial time series with missing blocks. Category: Benchmark. Observations: 5,000 (4,900 known, 100 missing). The dataset contains five non-consecutive blocks of 20 missing values each. Competitors were asked to predict these 100 unknown points, and performance was evaluated using MSE (E1 for all unknowns and E2 for the first 80 points).

Usage

data(CATS)

Format

A data frame with five columns and 980 rows. Each column represents a known segment of the time series.

Details

The CATS benchmark contains artificial series with five nonconsecutive missing blocks of 20 points each. Models must impute or forecast the missing blocks; evaluation typically uses MSE over all missing points.

Source

CATS Time Series Competition

References

Lendasse, A., Oja, E., Simula, O., Verleysen, M., et al. (2004). Time Series Prediction Competition: The CATS Benchmark. In IJCNN'2004 - International Joint Conference on Neural Networks. Lendasse, A., Oja, E., Simula, O., Verleysen, M. (2007). Time Series Prediction Competition: The CATS Benchmark. Neurocomputing, 70(13-15), 2325–2329.

Examples

# Load CATS dataset
data(CATS)
# CATS <- loadfulldata(CATS)

EUNITE Competition – Half-Hourly Electrical Loads

Description

Half-hourly electrical load time series from the EUNITE forecasting competition. Data Type: Electrical load measurements. Category: Benchmark. Observations: 730 days, 48 intervals per day. This dataset contains univariate time series with half-hour resolution covering 1997–1998. It was used to forecast daily maximum loads in January 1999. Competitors were evaluated using MAPE and MAXIMAL prediction errors. Regressors such as temperature and calendar variables were also provided.

Usage

data(EUNITE.Loads)

Format

A data frame with 730 rows and 48 numeric columns. Each column corresponds to one half-hour interval, from 00:00 to 24:00.

Details

The EUNITE competition focused on forecasting maximum daily electrical loads for January 1999 using half-hourly load profiles and auxiliary regressors. Series are provided in a wide format with 48 half-hour intervals as columns.

Source

EUNITE Competition 2001 dataset (original competition website currently unavailable).

References

Chen, B.-J., Chang, M.-W., & Lin, C.-J. (2004). Load forecasting using support vector machines: a study on EUNITE competition 2001. IEEE Transactions on Power Systems, 19(4), 1821-1830.

Examples

# Load the dataset
data(EUNITE.Loads)
# EUNITE.Loads <- loadfulldata(EUNITE.Loads)

# Inspect the first few half-hourly columns (00:00 to 24:00 by 30 minutes)
head(names(EUNITE.Loads))

# Plot a single half-hour interval across days
ts.plot(EUNITE.Loads[["X24.00"]], ylab = "Load (MW)", xlab = "Day",
        main = "EUNITE: Half-hour interval 24:00")

EUNITE Competition – Regressors for Load Forecasting

Description

Daily holiday and weekday indicators used as regressors in the EUNITE load forecasting competition. Data Type: Categorical indicators. Category: Benchmark. Observations: 730 (1997–1998). This dataset provides binary holiday flags and weekday identifiers to support the prediction of daily maximum electrical loads. It complements the datasets EUNITE.Loads and EUNITE.Temp. A test set with corresponding regressors for January 1999 is available.

Usage

data(EUNITE.Reg)

Format

A data frame with 730 rows and 3 columns:

Holiday

Binary indicator (1 = holiday, 0 = regular day).

Weekday

Integer encoding (1 = Sunday, ..., 7 = Saturday).

split

Split into train and test

Details

Regressors complement the load profiles by providing daily-level covariates (e.g., holidays and weekdays), which are known to improve forecast accuracy when used with temperature.

Source

EUNITE Competition 2001 dataset (original competition website currently unavailable).

References

Chen, B.-J., Chang, M.-W., & Lin, C.-J. (2004). Load forecasting using support vector machines: a study on EUNITE competition 2001. IEEE Transactions on Power Systems, 19(4), 1821-1830.

Examples

# Load EUNITE regressors
data(EUNITE.Reg)
# EUNITE.Reg <- loadfulldata(EUNITE.Reg)

# Peek at the first rows
head(EUNITE.Reg)

EUNITE Competition – Average Daily Temperatures

Description

Average daily temperatures collected for the EUNITE load-forecasting competition. Data Type: Meteorological measurements. Category: Benchmark. Observations: 1,461. The series covers 1995-1998 and was used as an exogenous regressor for predicting maximum daily electrical loads. Participants were asked to forecast January 1999 values.

Usage

data(EUNITE.Temp)

Format

A data frame with one numeric column and 1,461 rows (average daily temperature).

Details

Daily temperatures are commonly used as exogenous variables for load forecasting due to strong weather dependence. This series aligns with the period covered by EUNITE.Loads.

Source

EUNITE Competition 2001 dataset (original competition website currently unavailable).

References

Chen, B.-J., Chang, M.-W., & Lin, C.-J. (2004). Load forecasting using support vector machines: a study on EUNITE competition 2001. IEEE Transactions on Power Systems, 19(4), 1821-1830.

Examples

# Load daily temperature series
data(EUNITE.Temp)
# EUNITE.Temp <- loadfulldata(EUNITE.Temp)

# Plot temperature over time
ts.plot(EUNITE.Temp$Temperature, ylab = "Temperature (°C)", xlab = "Day",
        main = "EUNITE: Daily Temperature")

MSE

Description

Compute mean squared error (MSE) between actual and predicted values.

Usage

MSE.ts(actual, prediction)

Arguments

actual

Numeric vector of observed values.

prediction

Numeric vector of predicted values.

Details

MSE = mean((actual - prediction)^2).

Value

Numeric scalar with the MSE.

NN3 Time Series Competition - Dataset A

Description

Monthly time series from the NN3 forecasting competition. Data Type: Empirical business time series. Category: Benchmark. Observations: 50 to 126 per series, 111 series. The dataset contains 111 univariate monthly time series from real business processes. Each series has between 50 and 126 observations. Participants were asked to forecast the next 18 values, and performance was evaluated using the mean sMAPE across all series.

Usage

data(NN3)

Format

A data frame with up to 126 rows and 111 columns. Each column corresponds to a different univariate monthly time series.

Details

NN3 comprises monthly business time series with varying lengths. Forecast accuracy is typically evaluated using sMAPE across a fixed holdout horizon.

Source

NN3 Time Series Forecasting Competition

References

Crone, S.F., Hibon, M., & Nikolopoulos, K. (2011). Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. International Journal of Forecasting, 27(3), 635–660. NN3 Competition (2007). http://www.neural-forecasting-competition.com/NN3/index.htm

Examples

# Load NN3 dataset
data(NN3)
# NN3 <- loadfulldata(NN3)

# Select one series by name and plot
series <- NN3[["NN3_111"]]
ts.plot(series, ylab = "Value", xlab = "Month", main = "NN3 example series")

NN5 Time Series Competition

Description

Daily time series from the NN5 forecasting competition. Data Type: ATM withdrawal amounts. Category: Benchmark. Observations: 735 per series, 111 series. The dataset contains 111 univariate time series representing daily cash withdrawals from ATMs in England. Each series includes 735 observations and may contain missing values and multiple seasonal patterns. Participants were asked to forecast the next 56 values for each series, and performance was evaluated using the mean sMAPE across all series.

Usage

data(NN5)

Format

A data frame with 735 rows and 111 columns. Each column corresponds to a different univariate daily time series.

Details

NN5 consists of daily ATM withdrawal amounts with complex multiple seasonalities and occasional missing values. Forecasts are evaluated via sMAPE on a 56-day horizon.

Source

NN5 Time Series Forecasting Competition

References

Crone, S.F. (2008). Results of the NN5 Time Series Forecasting Competition. IEEE WCCI 2008, Hong Kong. NN5 Competition (2008). http://www.neural-forecasting-competition.com/NN5/index.htm

Examples

# Load NN5 dataset
data(NN5)
# NN5 <- loadfulldata(NN5)

# Select one series and plot
series <- NN5[["NN5.111"]]
ts.plot(series, ylab = "Withdrawals", xlab = "Day", main = "NN5 example series")

R2

Description

Compute coefficient of determination (R-squared).

Usage

R2.ts(actual, prediction)

Arguments

actual

Numeric vector of observed values.

prediction

Numeric vector of predicted values.

Value

Numeric scalar with R-squared.

Santa Fe Time Series Competition - Series A

Description

Univariate time series A from the Santa Fe Time Series Competition. Data Type: Laser-generated nonlinear time series. Category: Benchmark. Observations: 1,100. This benchmark dataset consists of a low-dimensional nonlinear and stationary series recorded from a Far-Infrared-Laser in a chaotic regime. Competitors were asked to predict the last 100 observations, and performance was evaluated using NMSE.

Usage

data(SantaFe.A)

Format

A data frame with one column and 1,100 rows, containing numeric time series values.

Details

Series A is a classic nonlinear laser dataset used to assess forecasting methods under chaotic dynamics.

Source

Santa Fe Time Series Competition dataset (original archive URL unavailable).

References

Weigend, A.S. (1993). Time Series Prediction: Forecasting the Future and Understanding the Past. Reading, MA: Westview Press.

Examples

# Load Santa Fe A series and plot
data(SantaFe.A)
# SantaFe.A <- loadfulldata(SantaFe.A)
series <- SantaFe.A$V1
ts.plot(series, ylab = "Value", xlab = "Index", main = "Santa Fe A")

Santa Fe Time Series Competition - Series D

Description

Univariate time series D from the Santa Fe Time Series Competition. Data Type: Simulated nonlinear time series. Category: Benchmark. Observations: 100,500. This benchmark dataset is composed of a four-dimensional nonlinear and non-stationary series. Competitors were asked to predict the last 500 observations, and performance was evaluated using NMSE.

Usage

data(SantaFe.D)

Format

A data frame with one column and 100,500 rows, containing numeric time series values.

Source

Santa Fe Time Series Competition dataset (original archive URL unavailable).

References

Weigend, A.S. (1993). Time Series Prediction: Forecasting the Future and Understanding the Past. Reading, MA: Westview Press.

Examples

# Load Santa Fe D series and plot a subset
data(SantaFe.D)
# SantaFe.D <- loadfulldata(SantaFe.D)
series <- SantaFe.D$V1
ts.plot(series[1:2000], ylab = "Value", xlab = "Index", main = "Santa Fe D (first 2000)")

Subset Extraction for Time Series Data

Description

Extracts a subset of a time series object based on specified rows and columns. The function allows for flexible indexing and subsetting of time series data.

Usage

## S3 method for class 'ts_data'
x[i, j, ...]

Arguments

x

ts_data object

i

row i

j

column j

...

optional arguments

Value

A new ts_data object with preserved metadata and column names.

Examples

data(tsd)
data10 <- ts_data(tsd$y, 10)
ts_head(data10)
#single line
data10[12,]

#range of lines
data10[12:13,]

#single column
data10[,1]

#range of columns
data10[,1:2]

#range of rows and columns
data10[12:13,1:2]

#single line and a range of columns
data10[12,1:2]

#range of lines and a single column
data10[12:13,1]

#single observation
data10[12,1]

Adjust ts_data

Description

Convert a compatible dataset to a ts_data object by setting column names, class, and the sw attribute consistently.

Usage

adjust_ts_data(data)

Arguments

data

Matrix or data.frame to adjust.

Value

An adjusted ts_data.

FAOSTAT Bioenergy Database

Description

Bioenergy data from FAOSTAT. Data Type: Bioenergy consumption and production. Category: Environment. Creation Date 2024.

Usage

data(bioenergy)

Format

A list of time series.

Details

Series are named as ⁠<country>_<bio_consumption|bio_production>⁠ and contain annual values.

Source

FAOSTAT Bioenergy Database

References

FAO 2024. FAOSTAT Bioenergy, FAO, Rome, Italy. ; United Nations Statistics Division (UNSD), 2011; International Recommendations for Energy Statistics (IRES).

Examples

# Load bioenergy list and plot one series
data(bioenergy)
# bioenergy <- loadfulldata(bioenergy)
series <- bioenergy[[1]]
ts.plot(series, ylab = "TJ", xlab = "Year", main = "Bioenergy example")

FAOSTAT Temperature Change on Land

Description

Statistics of surface temperature anomalies on land, based on NASA-GISS GISTEMP data. Data Type: Temperature Anomalies. Category: Environment. Creation Date 2024.

Usage

data(climate)

Format

A list of time series.

Source

NASA-GISS GISTEMP

References

FAO, 2024. FAOSTAT Land, Inputs and Sustainability; Climate Change Indicators; Temperature change on land. GISTEMP Team, 2024: GISS Surface Temperature Analysis. NASA Goddard Institute for Space Studies. Hansen, J. et al., 1981–2019: Multiple foundational studies on global temperature analysis.

Examples

# Load climate list and plot one series
data(climate)
# climate <- loadfulldata(climate)
series <- climate[[1]]
ts.plot(series, ylab = "Temperature change (°C)", xlab = "Year",
        main = "Temperature change on land")

Fit Time Series Model

Description

Generic for fitting a time series model. Descendants should implement ⁠do_fit.<class>⁠.

Usage

do_fit(obj, x, y = NULL)

Arguments

obj

Model object to be fitted.

x

Matrix or data.frame with input features.

y

Vector or matrix with target values.

Value

A fitted object (same class as obj).

Predict Time Series Model

Description

Generic for predicting with a fitted time series model. Descendants should implement ⁠do_predict.<class>⁠.

Usage

do_predict(obj, x)

Arguments

obj

Fitted model object.

x

Matrix or data.frame with input features to predict.

Value

Numeric vector with predicted values.

FAOSTAT Emissions Totals

Description

National and global estimates of greenhouse gas (GHG) emissions. Data Type: Greenhouse gas emissions. Category: Environment. Creation Date 2023.

Usage

data(emissions)

Format

A list of time series.

Source

FAOSTAT Emissions Totals.

References

FAO, 2023. FAOSTAT Climate Change: Agrifood systems emissions, Emissions Totals. IPCC Guidelines and Reports: 1996, 2000, 2006, 2014, 2019. PRIMAP-hist dataset v2.4.2: Gütschow et al., 2023.

Examples

# Load emissions list and plot one series
data(emissions)
# emissions <- loadfulldata(emissions)
series <- emissions[[1]]
ts.plot(series, ylab = "kt CO2e", xlab = "Year", main = "Emissions example (CH4/N2O)")

FAOSTAT Fertilizers by Nutrient

Description

Statistics on agricultural use, production, and trade of chemical and mineral fertilizers. Data Type: Fertilizers use, production and trade. Category: Environment. Creation Date 2024.

Usage

data(fertilizers)

Format

A list of time series.

Source

FAOSTAT Fertilizers by Nutrient.

References

FAO, 2024. FAOSTAT: Fertilizers by Nutrient. FAO & UNSD (2017). System of Environmental-Economic Accounting for Agriculture, Forestry and Fisheries (SEEA AFF). UNSD (2017). Framework for the Development of Environment Statistics (FDES).

Examples

# Load fertilizers list and plot one series
data(fertilizers)
# fertilizers <- loadfulldata(fertilizers)
series <- fertilizers[[1]]
ts.plot(series, ylab = "tonnes", xlab = "Year", main = "Fertilizers example")

Gross Domestic Product and Agriculture Value Added

Description

Summary of global and regional trends in GDP and agriculture value. Data Type: macroeconomic indicators. Category: Economy. Creation Date 2024.

Usage

data(gdp)

Format

list of time series.

Source

FAOSTAT Macro Indicators Database

References

FAO. 2024. Gross domestic product and agriculture value added 2013–2022 – Global and regional trends. FAOSTAT Analytical Briefs, No. 85. Rome. doi:10.4060/cd0763en

Examples

# Load GDP list and plot one series
data(gdp)
# gdp <- loadfulldata(gdp)
series <- gdp[[1]]
ts.plot(series, ylab = "US$", xlab = "Year", main = "GDP example")

Ipea Daily Macroeconomic Dataset

Description

Daily economic time series from Ipea (Institute for Applied Economic Research, Brazil). Data Type: Macroeconomic indicators. Category: Public data. Observations: 901 to 8,154 per series, 12 series. This dataset contains the most requested time series provided by Ipea with daily frequency, including exchange rates, stock index, interest rates, imports and exports. The series span from 1962 to September 2017. Missing values were removed using na.omit. The last 30 observations are for test set.

Usage

data(ipeadata.d)

Format

A data frame with up to 8,154 rows and 12 columns. Each column corresponds to a different univariate daily time series.

Details

Contains daily macroeconomic indicators frequently used in empirical forecasting. Series are cleaned with na.omit.

Source

Ipea - Ipeadata Portal, section "Most Requested Series", filtered by frequency "Daily".

References

Ipea (2017). Ipeadata – Macroeconomic and Regional Data. Technical Report. http://www.ipeadata.gov.br

Examples

# Load Ipea daily dataset and plot the first series
data(ipeadata.d)
# ipeadata.d <- loadfulldata(ipeadata.d)
series <- ipeadata.d[[1]]
ts.plot(series, ylab = "Value", xlab = "Day", main = "Ipea daily example")

Ipea Monthly Macroeconomic Dataset

Description

Monthly economic time series from Ipea (Institute for Applied Economic Research, Brazil). Data Type: Macroeconomic indicators. Category: Public data. Observations: 156 to 1019 per series, 23 series. This dataset contains the most requested time series provided by Ipea, including exchange rates, inflation indices, unemployment rates, interest rates, minimum wage, and GDP. The series span from 1930 to September 2017. Missing values were removed using na.omit. The last 12 observations are for testing set.

Usage

data(ipeadata.m)

Format

A data frame with up to 1019 rows and 23 columns. Each column corresponds to a different univariate monthly time series.

Details

Contains monthly macroeconomic indicators; the last 12 observations are intended as a test set.

Source

Ipea - Ipeadata Portal, section "Most Requested Series", filtered by frequency "Monthly".

References

Ipea (2017). Ipeadata – Macroeconomic and Regional Data. Technical Report. http://www.ipeadata.gov.br

Examples

# Load Ipea monthly dataset and plot the first series
data(ipeadata.m)
# ipeadata.m <- loadfulldata(ipeadata.m)
series <- ipeadata.m[[1]]
ts.plot(series, ylab = "Value", xlab = "Month", main = "Ipea monthly example")

Load Full Dataset From Mini Data Object

Description

Downloads and loads the full .RData object referenced by attr(x, "url") from a mini dataset object loaded from ⁠data/⁠.

Usage

loadfulldata(x)

Arguments

x

A mini dataset object that contains attr(x, "url").

Value

The full dataset object loaded from the remote .RData file.

M1 Competition Time Series

Description

Time series data from the first Makridakis forecasting competition (M1), held in 1982. Data Type: Forecasting benchmark dataset. Category: Forecasting. Creation Date: 1982.

Usage

data(m1)

Format

A list of dataframes containing time series.

Details

Consolidated list with frequencies as keys (e.g., monthly, quarterly, yearly). Each element is a list of series. See Makridakis et al. (1982) for competition design and evaluation.

Source

The accuracy of extrapolation (time series) methods: Results of a forecasting competition

References

Makridakis et al. (1982). The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting, 1(2), 111–153.

Examples

# Load consolidated M1 list
data(m1)
# m1 <- loadfulldata(m1)

# List available frequency keys
names(m1)

# Plot one series from a frequency bucket
series <- m1$monthly[[1]]
ts.plot(series, main = "M1 monthly series")

M3 Competition Time Series

Description

Time series data from the third Makridakis forecasting competition (M3), held in 2000. Data Type: Forecasting benchmark dataset. Category: Forecasting. Creation Date: 2000.

Usage

data(m3)

Format

A list of lists containing time series.

Details

Consolidated list keyed by frequency (e.g., monthly, other, quarterly, yearly). Each holds a list of numeric vectors. See Makridakis & Hibon (2000) for competition results and implications.

Source

doi:10.1016/S0169-2070(00)00057-1

References

Makridakis and Hibon (2000). The M3-Competition: Results, conclusions and implications. International Journal of Forecasting, 16(4), 451–476.

Examples

# Load consolidated M3 list and plot one monthly series
data(m3)
# m3 <- loadfulldata(m3)
series <- m3$monthly$M1
ts.plot(series, main = "M3 monthly series: M1")

M4 Competition Time Series

Description

Time series data from the fourth Makridakis forecasting competition (M4), held in 2018. Data Type: Forecasting benchmark dataset. Category: Forecasting. Creation Date: 2018.

Usage

data(m4)

Format

A list of lists containing time series.

Details

Consolidated list keyed by frequency (e.g., daily, hourly, monthly, ...). Each holds a list of numeric vectors. See Makridakis et al. (2020) for an overview of M4 findings.

Source

M4 Competition - GitHub

References

Makridakis et al. (2020). The M4 Competition: Results, findings, conclusion and way forward. International Journal of Forecasting, 36(1), 54–74.

Examples

# Load consolidated M4 list and plot one available series
data(m4)
# m4 <- loadfulldata(m4)
freq_name <- names(m4)[1]
series_name <- names(m4[[freq_name]])[1]
series <- m4[[freq_name]][[series_name]]
ts.plot(series, main = paste("M4", freq_name, "series:", series_name))

Pesticides Use Statistics

Description

Statistics on the use of major pesticide groups and relevant chemical families. Data Type: pesticides use. Category: Environments. Creation Date 2024.

Usage

data(pesticides)

Format

A list of time series.

Details

Series are named by country with ⁠_pesticides⁠ suffix; values are annual usage amounts.

Source

Pesticides Use Database

References

FAO. 2024. FAOSTAT: Pesticides Use. RP_e_README_Domain_Information_2024. FAOSTAT Pesticides Use Database

Examples

# Load pesticides list and plot one series
data(pesticides)
# pesticides <- loadfulldata(pesticides)
series <- pesticides[[1]]
ts.plot(series, ylab = "tonnes", xlab = "Year", main = "Pesticides example")

sMAPE

Description

Compute symmetric mean absolute percent error (sMAPE).

Usage

sMAPE.ts(actual, prediction)

Arguments

actual

Numeric vector of observed values.

prediction

Numeric vector of predicted values.

Details

sMAPE = mean( |a - p| / ((|a| + |p|)/2) ), excluding zero denominators.

Value

Numeric scalar with the sMAPE.

References

Select Optimal Hyperparameters for Time Series Models

Description

Identifies the optimal hyperparameters by minimizing the error from a dataset of hyperparameters. The function selects the hyperparameter configuration that results in the lowest average error. It wraps the dplyr library.

Usage

## S3 method for class 'ts_tune'
select_hyper(obj, hyperparameters)

Arguments

obj

a ts_tune object containing the model and tuning settings

hyperparameters

hyperparameters dataset

Value

returns the optimized key number of hyperparameters

IBOVESPA's 50 Most Traded Stocks

Description

Historical daily data for the 50 most traded stocks in B3 (IBOVESPA), including opening, high, low, and closing prices, as well as trading volume. Data Type: Financial Time Series. Category: Finance. Creation Date: 2025.

Usage

data(stocks)

Format

A list of dataframes containing time series.

Details

Each entry is a data frame with columns date, open, high, low, close, and volume.

Source

B3

References

B3 - Brasil, Bolsa, Balcão. 2025. Historical stock trading data. B3 Official Website

Examples

# Load stocks list and plot closing prices for a ticker (if present)
data(stocks)
# stocks <- loadfulldata(stocks)
if ("VALE3" %in% names(stocks)) {
  series <- stocks$VALE3$close
  ts.plot(series, ylab = "Close", xlab = "Index", main = "VALE3 close price")
}

ARIMA

Description

Create a time series prediction object based on the AutoRegressive Integrated Moving Average (ARIMA) family.

This constructor sets up an S3 time series regressor that leverages the forecast package to automatically select orders via auto.arima and provide one-step and multi-step forecasts.

Usage

ts_arima()

Details

ARIMA models combine autoregressive (AR), differencing (I), and moving average (MA) components to model temporal dependence in a univariate time series. The fit() method uses forecast::auto.arima() to select orders using information criteria, and predict() supports both a single one-step-ahead over a horizon (rolling) and direct multi-step forecasting.

Assumptions include (after differencing) approximate stationarity and homoskedastic residuals. Always inspect residual diagnostics for adequacy.

Value

A ts_arima object (S3), which inherits from ts_reg.

References

Examples

# Example: rolling-origin evaluation with multi-step prediction
# Load package and dataset
library(daltoolbox)
data(tsd)

# 1) Wrap the raw vector as `ts_data` without sliding windows
ts <- ts_data(tsd$y, 0)
ts_head(ts, 3)

# 2) Split into train/test using the last 5 observations as test
samp <- ts_sample(ts, test_size = 5)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

# 3) Fit ARIMA via auto.arima
model <- ts_arima()
model <- fit(model, x = io_train$input, y = io_train$output)

# 4) Predict 5 steps ahead from the most recent observed point
prediction <- predict(model, x = io_test$input[1,], steps_ahead = 5)
prediction <- as.vector(prediction)
output <- as.vector(io_test$output)

# 5) Evaluate forecast accuracy
ev_test <- evaluate(model, output, prediction)
ev_test

Augmentation by Awareness

Description

Bias the augmentation to emphasize more recent points in each window (recency awareness), increasing their contribution to the augmented sample.

Usage

ts_aug_awareness(factor = 1)

Arguments

factor

Numeric factor controlling the recency weighting.

Value

A ts_aug_awareness object.

References

Examples

# Recency-aware augmentation over sliding windows
# Load package and example dataset
library(daltoolbox)
data(tsd)

# Convert to 10-lag sliding windows and preview
xw <- ts_data(tsd$y, 10)
ts_head(xw)

# Apply awareness augmentation (bias toward recent rows)
augment <- ts_aug_awareness()
augment <- fit(augment, xw)
xa <- transform(augment, xw)
ts_head(xa)

Augmentation by Awareness Smooth

Description

Recency-aware augmentation that also progressively smooths noise before applying the weighting, producing cleaner augmented samples.

Usage

ts_aug_awaresmooth(factor = 1)

Arguments

factor

Numeric factor controlling the recency weighting.

Value

A ts_aug_awaresmooth object.

References

Examples

# Recency-aware augmentation with progressive smoothing
# Load package and example dataset
library(daltoolbox)
data(tsd)

# Convert to 10-lag sliding windows and preview
xw <- ts_data(tsd$y, 10)
ts_head(xw)

# Apply awareness+smooth augmentation and inspect result
augment <- ts_aug_awaresmooth()
augment <- fit(augment, xw)
xa <- transform(augment, xw)
ts_head(xa)

Augmentation by Flip

Description

Time series augmentation by mirroring sliding-window observations around their mean to increase diversity and reduce overfitting.

Usage

ts_aug_flip()

Details

This transformation preserves the window mean while flipping the deviations, effectively generating a symmetric variant of the local pattern.

Value

A ts_aug_flip object.

References

Examples

# Flip augmentation around the window mean
# Load package and example dataset
library(daltoolbox)
data(tsd)

# Convert to sliding windows and preview
xw <- ts_data(tsd$y, 10)
ts_head(xw)

# Apply flip augmentation and inspect augmented windows
augment <- ts_aug_flip()
augment <- fit(augment, xw)
xa <- transform(augment, xw)
ts_head(xa)

Augmentation by Jitter

Description

Time series augmentation by adding low-amplitude random noise to each point to increase robustness and reduce overfitting.

Usage

ts_aug_jitter()

Details

Noise scale is estimated from within-window deviations.

Value

A ts_aug_jitter object.

References

Examples

# Jitter augmentation with noise estimated from windows
# Load package and example dataset
library(daltoolbox)
data(tsd)

# Convert to sliding windows and preview
xw <- ts_data(tsd$y, 10)
ts_head(xw)

# Apply jitter (adds small noise; keeps target column unchanged)
augment <- ts_aug_jitter()
augment <- fit(augment, xw)
xa <- transform(augment, xw)
ts_head(xa)

No Augmentation

Description

Identity augmentation that returns the original windows while preserving the augmentation interface and indices.

Usage

ts_aug_none()

Value

A ts_aug_none object.

Examples

# Identity augmentation (no changes to windows)
# Load package and example dataset
library(daltoolbox)
data(tsd)

# Convert to sliding windows and preview
xw <- ts_data(tsd$y, 10)
ts_head(xw)

# No augmentation; returns the same windows with indices preserved
augment <- ts_aug_none()
augment <- fit(augment, xw)
xa <- transform(augment, xw)
ts_head(xa)

Augmentation by Shrink

Description

Decrease within-window deviation magnitude by a scaling factor to generate lower-variance variants while preserving the mean.

Usage

ts_aug_shrink(scale_factor = 0.8)

Arguments

scale_factor

Numeric factor used to scale deviations.

Value

A ts_aug_shrink object.

References

Examples

# Shrink augmentation reduces within-window deviations
# Load package and example dataset
library(daltoolbox)
data(tsd)

# Convert to sliding windows and preview
xw <- ts_data(tsd$y, 10)
ts_head(xw)

# Apply shrink augmentation and inspect augmented windows
augment <- ts_aug_shrink()
augment <- fit(augment, xw)
xa <- transform(augment, xw)
ts_head(xa)

Augmentation by Stretch

Description

Increase within-window deviation magnitude by a scaling factor to produce higher-variance variants.

Usage

ts_aug_stretch(scale_factor = 1.2)

Arguments

scale_factor

Numeric factor used to scale deviations.

Value

A ts_aug_stretch object.

References

Examples

# Stretch augmentation increases within-window deviations
# Load package and example dataset
library(daltoolbox)
data(tsd)

# Convert to sliding windows and preview
xw <- ts_data(tsd$y, 10)
ts_head(xw)

# Apply stretch augmentation and inspect augmented windows
augment <- ts_aug_stretch()
augment <- fit(augment, xw)
xa <- transform(augment, xw)
ts_head(xa)

Augmentation by Wormhole

Description

Generate augmented windows by selectively replacing lag terms with older lagged values, creating plausible alternative trajectories.

Usage

ts_aug_wormhole()

Details

This combinatorial replacement preserves overall scale while introducing temporal permutations of lag content.

Value

A ts_aug_wormhole object.

References

Examples

# Wormhole augmentation replaces some lags with older values
# Load package and example dataset
library(daltoolbox)
data(tsd)

# Convert to sliding windows and preview
xw <- ts_data(tsd$y, 10)
ts_head(xw)

# Apply wormhole augmentation and inspect augmented windows
augment <- ts_aug_wormhole()
augment <- fit(augment, xw)
xa <- transform(augment, xw)
ts_head(xa)

ts_data

Description

Construct a time series data object used throughout the DAL Toolbox.

Accepts either a vector (raw time series) or a matrix/data.frame already organized in sliding windows. Internally, a ts_data is stored as a matrix with sw lag columns named ⁠t{lag}⁠ (e.g., ⁠t9, t8, ..., t0⁠). When sw is zero or one, the series is stored as a single column (t0).

Usage

ts_data(y, sw = 1)

Arguments

y

Numeric vector or matrix-like. Time series values or sliding windows.

sw

Integer. Sliding-window size (number of lag columns).

Value

A ts_data object (matrix with attributes and column names).

Examples

# Example: building sliding windows
data(tsd)
head(tsd)

# 1) Single-column ts_data (no windows)
data <- ts_data(tsd$y)
ts_head(data)

# 2) 10-lag sliding windows (t9 ... t0)
data10 <- ts_data(tsd$y, 10)
ts_head(data10)

ELM

Description

Create a time series prediction object that uses Extreme Learning Machine (ELM) regression.

It wraps the elmNNRcpp package to train single-hidden-layer networks with randomly initialized hidden weights and closed-form output weights.

Usage

ts_elm(preprocess = NA, input_size = NA, nhid = NA, actfun = "purelin")

Arguments

preprocess

Normalization preprocessor (e.g., ts_norm_gminmax()).

input_size

Integer. Number of lagged inputs used by the model.

nhid

Integer. Hidden layer size.

actfun

Character. One of 'sig', 'radbas', 'tribas', 'relu', 'purelin'.

Details

ELMs are efficient to train and can perform well with appropriate hidden size and activation choice. Consider normalizing inputs and tuning nhid and the activation function.

Value

A ts_elm object (S3) inheriting from ts_regsw.

References

Examples

# Example: ELM with sliding-window inputs
# Load package and toy dataset
library(daltoolbox)
data(tsd)

# Create sliding windows of length 10 (t9 ... t0)
ts <- ts_data(tsd$y, 10)
ts_head(ts, 3)

# Split last 5 rows as test set
samp <- ts_sample(ts, test_size = 5)
# Project to inputs (X) and outputs (y)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

# Define ELM with global min-max normalization and fit
model <- ts_elm(ts_norm_gminmax(), input_size = 4, nhid = 3, actfun = "purelin")
model <- fit(model, x = io_train$input, y = io_train$output)

# Forecast 5 steps ahead starting from the last known window
prediction <- predict(model, x = io_test$input[1,], steps_ahead = 5)
prediction <- as.vector(prediction)
output <- as.vector(io_test$output)

# Evaluate forecast error on the test horizon
ev_test <- evaluate(model, output, prediction)
ev_test

Exponential Moving Average (EMA)

Description

Smooth a series by exponentially decaying weights that give more importance to recent observations.

Usage

ts_fil_ema(ema = 3)

Arguments

ema

exponential moving average size

Details

EMA is related to simple exponential smoothing; it reacts faster to level changes than a simple moving average while reducing noise.

Value

A ts_fil_ema object.

References

Examples

# Exponential moving average smoothing on a noisy series
# Load package and example data
library(daltoolbox)
data(tsd)

# Inject an outlier to illustrate smoothing effect
tsd$y[9] <- 2 * tsd$y[9]

# Define EMA filter, fit and transform the series
filter <- ts_fil_ema(ema = 3)
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# Compare original vs smoothed series
plot_ts_pred(y = tsd$y, yadj = y)

EMD Filter

Description

Empirical Mode Decomposition (EMD) filter that decomposes a signal into intrinsic mode functions (IMFs) and reconstructs a smoothed component.

Usage

ts_fil_emd(noise = 0.1, trials = 5)

Arguments

noise

noise

trials

trials

Value

A ts_fil_emd object.

References

Examples

# EMD-based smoothing: remove first IMF as noise
# Load package and example data
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2 * tsd$y[9]  # inject an outlier

# Fit EMD filter and reconstruct without the first (noisiest) IMF
filter <- ts_fil_emd()
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# Compare original vs smoothed series
plot_ts_pred(y = tsd$y, yadj = y)

FFT Filter

Description

Frequency-domain smoothing using the Fast Fourier Transform (FFT) to attenuate high-frequency components.

Usage

ts_fil_fft()

Details

The implementation estimates a cutoff based on spectral statistics and reconstructs the series from dominant frequencies.

Value

A ts_fil_fft object.

References

Examples

# Frequency-domain smoothing via FFT cutoff
# Load package and example data
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2 * tsd$y[9]  # inject an outlier

# Fit FFT-based filter and reconstruct without high frequencies
filter <- ts_fil_fft()
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# Compare original vs frequency-smoothed series
plot_ts_pred(y = tsd$y, yadj = y)

Hodrick-Prescott Filter

Description

Decompose a series into trend and cyclical components using the Hodrick–Prescott (HP) filter and optionally blend with the original series.

This filter removes short-term fluctuations by penalizing changes in the growth rate of the trend component.

Usage

ts_fil_hp(lambda = 100, preserve = 0.9)

Arguments

lambda

It is the smoothing parameter of the Hodrick-Prescott filter. Lambda = 100*(frequency)^2 Correspondence between frequency and lambda values annual => frequency = 1 // lambda = 100 quarterly => frequency = 4 // lambda = 1600 monthly => frequency = 12 // lambda = 14400 weekly => frequency = 52 // lambda = 270400 daily (7 days a week) => frequency = 365 // lambda = 13322500 daily (5 days a week) => frequency = 252 // lambda = 6812100

preserve

value between 0 and 1. Balance the composition of observations and applied filter. Values close to 1 preserve original values. Values close to 0 adopts HP filter values.

Details

The filter strength is governed by lambda = 100 * frequency^2. Use preserve in (0, 1] to convex-combine the raw series and the HP trend.

Value

A ts_fil_hp object.

References

Examples

# time series with noise
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2*tsd$y[9]

# filter
filter <- ts_fil_hp(lambda = 100*(26)^2)  #frequency assumed to be 26
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# plot
plot_ts_pred(y=tsd$y, yadj=y)

Kalman Filter

Description

Estimate a latent trend via a state-space model using the Kalman Filter (KF), wrapping the KFAS package.

Usage

ts_fil_kalman(H = 0.1, Q = 1)

Arguments

H

variance or covariance matrix of the measurement noise. This noise pertains to the relationship between the true system state and actual observations. Measurement noise is added to the measurement equation to account for uncertainties or errors associated with real observations. The higher this value, the higher the level of uncertainty in the observations.

Q

variance or covariance matrix of the process noise. This noise follows a zero-mean Gaussian distribution. It is added to the equation to account for uncertainties or unmodeled disturbances in the state evolution. The higher this value, the greater the uncertainty in the state transition process.

Value

A ts_fil_kalman object.

References

Examples

# State-space smoothing with Kalman Filter (KF)
# Load package and example data
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2 * tsd$y[9]  # inject an outlier

# Fit KF (H = obs noise, Q = process noise) and transform
filter <- ts_fil_kalman()
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# Plot original vs KF-smoothed series
plot_ts_pred(y = tsd$y, yadj = y)

LOWESS Smoothing

Description

Locally Weighted Scatterplot Smoothing (LOWESS) fits local regressions to capture the primary trend while reducing noise and spikes.

Usage

ts_fil_lowess(f = 0.2)

Arguments

f

smoothing parameter. The larger this value, the smoother the series will be. This provides the proportion of points on the plot that influence the smoothing.

Value

A ts_fil_lowess object.

References

Examples

# time series with noise
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2*tsd$y[9]

# filter
filter <- ts_fil_lowess(f = 0.2)
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# plot
plot_ts_pred(y=tsd$y, yadj=y)

Moving Average (MA)

Description

Smooth out fluctuations and reduce noise by averaging over a fixed-size rolling window.

Usage

ts_fil_ma(ma = 3)

Arguments

ma

moving average size

Details

Larger windows produce smoother series but may lag turning points.

Value

A ts_fil_ma object.

Examples

# time series with noise
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2*tsd$y[9]

# filter
filter <- ts_fil_ma(3)
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# plot
plot_ts_pred(y=tsd$y, yadj=y)

No Filter

Description

Identity filter that returns the original series unchanged.

Usage

ts_fil_none()

Value

A ts_fil_none object.

Examples

# Identity filter (returns original series)
# Load package and example series
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2 * tsd$y[9]  # inject an outlier for comparison

# Fit identity filter and transform (no change expected)
filter <- ts_fil_none()
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# Plot original vs (identical) filtered series
plot_ts_pred(y = tsd$y, yadj = y)

Quadratic Exponential Smoothing

Description

Double/triple exponential smoothing capturing level, trend, and optionally seasonality components.

Usage

ts_fil_qes(gamma = FALSE)

Arguments

gamma

If TRUE, enables the gamma seasonality component.

Value

A ts_fil_qes object.

References

Examples

# time series with noise
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2*tsd$y[9]

# filter
filter <- ts_fil_qes()
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# plot
plot_ts_pred(y=tsd$y, yadj=y)

Recursive Filter

Description

Apply recursive linear filtering (ARMA-style recursion) to a univariate series or each column of a multivariate series. Useful for smoothing and mitigating autocorrelation.

Usage

ts_fil_recursive(filter)

Arguments

filter

smoothing parameter. The larger the value, the greater the smoothing. The smaller the value, the less smoothing, and the resulting series shape is more similar to the original series.

Value

A ts_fil_recursive object.

Examples

# time series with noise
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2*tsd$y[9]

# filter
filter <- ts_fil_recursive(filter =  0.05)
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# plot
plot_ts_pred(y=tsd$y, yadj=y)

Robust EMD Filter

Description

Ensemble/robust EMD-based denoising using CEEMD to separate noise-dominated IMFs and reconstruct the signal.

Usage

ts_fil_remd(noise = 0.1, trials = 5)

Arguments

noise

noise

trials

trials

Value

A ts_fil_remd object.

References

Examples

# time series with noise
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2*tsd$y[9]

# filter
filter <- ts_fil_remd()
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# plot
plot_ts_pred(y=tsd$y, yadj=y)

Seasonal Adjustment

Description

Remove the seasonal component from a time series while preserving level and trend, using a state-space/BATS approach.

Usage

ts_fil_seas_adj(frequency = NULL)

Arguments

frequency

Frequency of the time series. It is an optional parameter. It can be configured when the frequency of the time series is known.

Value

A ts_fil_seas_adj object.

References

Examples

# Seasonal adjustment using BATS at known frequency
# Load package and example data
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2 * tsd$y[9]  # inject an outlier (illustrative)

# Fit seasonal adjustment (set frequency if known) and transform
filter <- ts_fil_seas_adj(frequency = 26)
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# Plot original vs seasonally adjusted series
plot_ts_pred(y = tsd$y, yadj = y)

Simple Exponential Smoothing

Description

Exponential smoothing focused on the level component, with optional extensions to trend/seasonality via Holt–Winters variants.

Usage

ts_fil_ses(gamma = FALSE)

Arguments

gamma

If TRUE, enables the gamma seasonality component.

Value

A ts_fil_ses object.

References

Examples

# time series with noise
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2*tsd$y[9]

# filter
filter <- ts_fil_ses()
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# plot
plot_ts_pred(y=tsd$y, yadj=y)

Time Series Smooth

Description

Remove or reduce randomness (noise) using a robust smoothing strategy that first mitigates outliers and then smooths residual variation.

Usage

ts_fil_smooth()

Value

A ts_fil_smooth object.

Examples

# Robust smoothing with iterative outlier mitigation
# Load package and example data
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2 * tsd$y[9]  # inject an outlier

# Fit smoother and transform to reduce spikes/noise
filter <- ts_fil_smooth()
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# Compare original vs smoothed series
plot_ts_pred(y = tsd$y, yadj = y)

Smoothing Splines

Description

Fit a cubic smoothing spline to a time series for smooth trend extraction with a tunable roughness penalty.

Usage

ts_fil_spline(spar = NULL)

Arguments

spar

smoothing parameter. When spar is specified, the coefficient of the integral of the squared second derivative in the fitting criterion (penalized log-likelihood) is a monotone function of spar.

Value

A ts_fil_spline object.

References

Examples

# Smoothing splines with adjustable roughness penalty
# Load package and example data
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2 * tsd$y[9]  # inject an outlier

# Fit spline smoother (spar controls smoothness) and transform
filter <- ts_fil_spline(spar = 0.5)
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# Compare original vs smoothed series
plot_ts_pred(y = tsd$y, yadj = y)

Wavelet Filter

Description

Denoise a series using discrete wavelet transforms and selected wavelet families.

Usage

ts_fil_wavelet(filter = "haar")

Arguments

filter

Available wavelet filters: 'haar', 'd4', 'la8', 'bl14', 'c6'.

Value

A ts_fil_wavelet object.

References

Examples

# Denoising with discrete wavelets (optionally selecting best filter)
# Load package and example data
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2 * tsd$y[9]  # inject an outlier

# Fit wavelet filter ("haar" by default; can pass a list to select best)
filter <- ts_fil_wavelet()
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# Compare original vs wavelet-denoised series
plot_ts_pred(y = tsd$y, yadj = y)

Winsorization of Time Series

Description

Apply Winsorization to limit extreme values by replacing them with nearer order statistics, reducing the influence of outliers.

Usage

ts_fil_winsor()

Value

A ts_fil_winsor object.

References

Examples

# Winsorization: cap extreme values to reduce outlier impact
# Load package and example data
library(daltoolbox)
data(tsd)
tsd$y[9] <- 2 * tsd$y[9]  # inject an outlier

# Fit Winsor filter and transform series
filter <- ts_fil_winsor()
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# Plot original vs Winsorized series
plot_ts_pred(y = tsd$y, yadj = y)

Extract the First Observations from a ts_data Object

Description

Return the first n observations from a ts_data.

Usage

ts_head(x, n = 6L, ...)

Arguments

x

ts_data object

n

number of rows to return

...

optional arguments

Value

The first n observations of a ts_data (as a matrix/data.frame).

Examples

data(tsd)
data10 <- ts_data(tsd$y, 10)
ts_head(data10)

Time Series Integrated Tune

Description

Integrated tuning over input sizes, preprocessing, augmentation, and model hyperparameters for time series.

Usage

ts_integtune(
  input_size,
  base_model,
  folds = 10,
  ranges = NULL,
  preprocess = list(ts_norm_gminmax()),
  augment = list(ts_aug_none())
)

Arguments

input_size

Integer vector. Candidate input window sizes.

base_model

Base model object for tuning.

folds

Integer. Number of cross-validation folds.

ranges

Named list of hyperparameter ranges to explore.

preprocess

List of preprocessing objects to compare.

augment

List of augmentation objects to apply during training.

Value

A ts_integtune object.

References

Salles, R., Pacitti, E., Bezerra, E., Marques, C., Pacheco, C., Oliveira, C., Porto, F., Ogasawara, E. (2023). TSPredIT: Integrated Tuning of Data Preprocessing and Time Series Prediction Models. Lecture Notes in Computer Science.

Examples

# Integrated search over input size, preprocessing and model hyperparameters
library(daltoolbox)
data(tsd)

# Build windows and split into train/test, then project to (X, y)
ts <- ts_data(tsd$y, 10)
samp <- ts_sample(ts, test_size = 5)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

# Configure integrated tuning: ranges for input_size, ELM (nhid, actfun), and preprocessors
tune <- ts_integtune(
  input_size = 3:5,
  base_model = ts_elm(),
  ranges = list(nhid = 1:5, actfun = c('purelin')),
  preprocess = list(ts_norm_gminmax())
)

# Run search; augmentation (if provided) is applied during training internally
model <- fit(tune, x = io_train$input, y = io_train$output)

# Forecast and evaluate on the held-out window
prediction <- predict(model, x = io_test$input[1,], steps_ahead = 5)
prediction <- as.vector(prediction)
output <- as.vector(io_test$output)

ev_test <- evaluate(model, output, prediction)
ev_test

KNN Time Series Prediction

Description

Create a prediction object that uses the K-Nearest Neighbors regression for time series via sliding windows.

Usage

ts_knn(preprocess = NA, input_size = NA, k = NA)

Arguments

preprocess

Normalization preprocessor (e.g., ts_norm_gminmax()).

input_size

Integer. Number of lagged inputs.

k

Integer. Number of neighbors.

Details

KNN regression predicts a value as the average (or weighted average) of the outputs of the k most similar windows in the training set. Similarity is computed in the feature space induced by lagged inputs. Consider normalization for distance-based methods.

Value

A ts_knn object (S3) inheriting from ts_regsw.

References

Examples

# Example: distance-based regression on sliding windows
# Load tools and example series
library(daltoolbox)
data(tsd)

# Build 10-lag windows and preview a few rows
ts <- ts_data(tsd$y, 10)
ts_head(ts, 3)

# Split end of series as test and project (X, y)
samp <- ts_sample(ts, test_size = 5)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

# Define KNN regressor and fit (distance-based; normalization recommended)
model <- ts_knn(ts_norm_gminmax(), input_size = 4, k = 3)
model <- fit(model, x = io_train$input, y = io_train$output)

# Predict multiple steps ahead and evaluate
prediction <- predict(model, x = io_test$input[1,], steps_ahead = 5)
prediction <- as.vector(prediction)
output <- as.vector(io_test$output)

ev_test <- evaluate(model, output, prediction)
ev_test

MLP

Description

Create a time series prediction object based on a Multilayer Perceptron (MLP) regressor.

It wraps the nnet package to train a single-hidden-layer neural network on sliding-window inputs. Use ts_regsw utilities to project inputs/outputs.

Usage

ts_mlp(preprocess = NA, input_size = NA, size = NA, decay = 0.01, maxit = 1000)

Arguments

preprocess

Normalization preprocessor (e.g., ts_norm_gminmax()).

input_size

Integer. Number of lagged inputs used by the model.

size

Integer. Number of hidden neurons.

decay

Numeric. L2 weight decay (regularization) parameter.

maxit

Integer. Maximum number of training iterations.

Details

The MLP is a universal function approximator capable of learning non-linear mappings from lagged inputs to next-step values. For stability, consider normalizing inputs (e.g., ts_norm_gminmax()). Hidden size and weight decay control capacity and regularization respectively.

Value

A ts_mlp object (S3) inheriting from ts_regsw.

References

Examples

# Example: MLP on sliding windows with min–max normalization
# Load package and dataset
library(daltoolbox)
data(tsd)
ts <- ts_data(tsd$y, 10)
ts_head(ts, 3)

samp <- ts_sample(ts, test_size = 5)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

# Prepare projection (X, y)
samp <- ts_sample(ts, test_size = 5)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

# Define and fit the MLP
model <- ts_mlp(ts_norm_gminmax(), input_size = 4, size = 4, decay = 0)
model <- fit(model, x=io_train$input, y=io_train$output)

# Predict 5 steps ahead
prediction <- predict(model, x = io_test$input[1,], steps_ahead = 5)
prediction <- as.vector(prediction)
output <- as.vector(io_test$output)

# Evaluate
ev_test <- evaluate(model, output, prediction)
ev_test

Adaptive Normalization

Description

Transform data to a common scale while adapting to changes in distribution over time (optionally over a trailing window).

Usage

ts_norm_an(outliers = outliers_boxplot(), nw = 0)

Arguments

outliers

Indicate outliers transformation class. NULL can avoid outliers removal.

nw

integer: window size.

Value

A ts_norm_an object.

References

Ogasawara, E., Martinez, L. C., De Oliveira, D., Zimbrão, G., Pappa, G. L., Mattoso, M. (2010). Adaptive Normalization: A novel data normalization approach for non-stationary time series. Proceedings of the International Joint Conference on Neural Networks (IJCNN). doi:10.1109/IJCNN.2010.5596746

Examples

# time series to normalize
library(daltoolbox)
data(tsd)

# convert to sliding windows
ts <- ts_data(tsd$y, 10)
ts_head(ts, 3)
summary(ts[,10])

# normalization
preproc <- ts_norm_an()
preproc <- fit(preproc, ts)
tst <- transform(preproc, ts)
ts_head(tst, 3)
summary(tst[,10])

First Differences

Description

Transform a series by first differences to remove level and highlight changes; normalization is then applied to the differenced series.

Usage

ts_norm_diff(outliers = outliers_boxplot())

Arguments

outliers

Indicate outliers transformation class. NULL can avoid outliers removal.

Value

A ts_norm_diff object.

References

Salles, R., Assis, L., Guedes, G., Bezerra, E., Porto, F., Ogasawara, E. (2017). A framework for benchmarking machine learning methods using linear models for univariate time series prediction. Proceedings of the International Joint Conference on Neural Networks (IJCNN). doi:10.1109/IJCNN.2017.7966139

Examples

# Differencing + global min–max normalization
# Load package and example data
library(daltoolbox)
data(tsd)

# Convert to sliding windows and preview raw last column
ts <- ts_data(tsd$y, 10)
ts_head(ts, 3)
summary(ts[,10])

# Fit differencing preprocessor and transform; note one fewer lag column
preproc <- ts_norm_diff()
preproc <- fit(preproc, ts)
tst <- transform(preproc, ts)
ts_head(tst, 3)
summary(tst[,9])

Adaptive Normalization with EMA

Description

Normalize a time series using exponentially weighted statistics that adapt to distributional changes, optionally after outlier mitigation.

Usage

ts_norm_ean(outliers = outliers_boxplot(), nw = 0)

Arguments

outliers

Indicate outliers transformation class. NULL can avoid outliers removal.

nw

windows size

Value

A ts_norm_ean object.

References

Ogasawara, E., Martinez, L. C., De Oliveira, D., Zimbrão, G., Pappa, G. L., Mattoso, M. (2010). Adaptive Normalization: A novel data normalization approach for non-stationary time series. Proceedings of the International Joint Conference on Neural Networks (IJCNN). doi:10.1109/IJCNN.2010.5596746

Examples

# time series to normalize
library(daltoolbox)
data(tsd)

# convert to sliding windows
ts <- ts_data(tsd$y, 10)
ts_head(ts, 3)
summary(ts[,10])

# normalization
preproc <- ts_norm_ean()
preproc <- fit(preproc, ts)
tst <- transform(preproc, ts)
ts_head(tst, 3)
summary(tst[,10])

Global Min–Max Normalization

Description

Rescale values so the global minimum maps to 0 and the global maximum maps to 1 over the training set.

Usage

ts_norm_gminmax(outliers = outliers_boxplot())

Arguments

outliers

Indicate outliers transformation class. NULL can avoid outliers removal.

Details

The same scaling is applied to inputs and inverted on predictions via inverse_transform.

Value

A ts_norm_gminmax object.

References

Ogasawara, E., Murta, L., Zimbrão, G., Mattoso, M. (2009). Neural networks cartridges for data mining on time series. Proceedings of the International Joint Conference on Neural Networks (IJCNN). doi:10.1109/IJCNN.2009.5178615

Examples

# Global min–max normalization across the full training set
# Load package and example data
library(daltoolbox)
data(tsd)

# Build 10-lag windows and preview raw scale
ts <- ts_data(tsd$y, 10)
ts_head(ts, 3)
summary(ts[,10])

# Fit global min–max and transform; inspect post-scale values
preproc <- ts_norm_gminmax()
preproc <- fit(preproc, ts)
tst <- transform(preproc, ts)
ts_head(tst, 3)
summary(tst[,10])

No Normalization

Description

Identity transform that leaves data unchanged but aligns with the pre/post-processing interface.

Usage

ts_norm_none()

Value

A ts_norm_none object.

Examples

# Identity normalization (no scaling applied)
# Load package and example data
library(daltoolbox)
data(tsd)

# Convert to sliding windows
xw <- ts_data(tsd$y, 10)

# No data normalization — transform returns inputs unchanged
normalize <- ts_norm_none()
normalize <- fit(normalize, xw)
xa <- transform(normalize, xw)
ts_head(xa)

Sliding-Window Min–Max Normalization

Description

Create an object for normalizing each window by its own min and max, preserving local contrast while standardizing scales.

Usage

ts_norm_swminmax(outliers = outliers_boxplot())

Arguments

outliers

Indicate outliers transformation class. NULL can avoid outliers removal.

Value

A ts_norm_swminmax object.

References

Ogasawara, E., Murta, L., Zimbrão, G., Mattoso, M. (2009). Neural networks cartridges for data mining on time series. Proceedings of the International Joint Conference on Neural Networks (IJCNN). doi:10.1109/IJCNN.2009.5178615

Examples

# Per-window min–max normalization for sliding windows
# Load package and example data
library(daltoolbox)
data(tsd)

# Build 10-lag windows and preview raw scale
ts <- ts_data(tsd$y, 10)
ts_head(ts, 3)
summary(ts[,10])

# Fit per-window min–max and transform; inspect post-scale values
preproc <- ts_norm_swminmax()
preproc <- fit(preproc, ts)
tst <- transform(preproc, ts)
ts_head(tst, 3)
summary(tst[,10])

Time Series Projection

Description

Split a ts_data (sliding windows) into input features and output targets for modeling.

Usage

ts_projection(ts)

Arguments

ts

Matrix or data.frame containing a ts_data representation.

Details

For a multi-column ts_data, returns all but the last column as inputs and the last column as the output. For a single-row matrix, returns ts_data-wrapped inputs/outputs preserving names and window size.

Value

A ts_projection object with two elements: ⁠$input⁠ and ⁠$output⁠.

Examples

# Setting up a ts_data and projecting (X, y)
# Load example dataset and create windows
data(tsd)
ts <- ts_data(tsd$y, 10)

io <- ts_projection(ts)

# Input data (features)
ts_head(io$input)

# Output data (target)
ts_head(io$output)

TSReg

Description

Base class for time series regression models that operate directly on time series (non-sliding-window specialization).

Usage

ts_reg()

Details

This class is intended to be subclassed by modeling backends that do not require the sliding-window interface. Methods such as fit(), predict(), and evaluate() dispatch on this class.

Value

A ts_reg object (S3) to be extended by concrete models.

Examples

# Abstract base class — instantiate concrete subclasses instead
# Examples: ts_mlp(), ts_rf(), ts_svm(), ts_arima()

TSRegSW

Description

Base class for time series regression models built on sliding-window representations.

Usage

ts_regsw(preprocess = NA, input_size = NA)

Arguments

preprocess

Normalization preprocessor (e.g., ts_norm_gminmax()).

input_size

Integer. Number of lagged inputs per example.

Details

This class provides helpers to map ts_data matrices into the input window expected by ML backends and to apply pre/post processing (e.g., normalization) consistently during fit and predict.

Value

A ts_regsw object (S3) to be extended by concrete models.

Examples

# Abstract base class for sliding-window regressors
# Use concrete subclasses such as ts_mlp(), ts_rf(), ts_svm(), ts_elm()

Random Forest

Description

Create a time series prediction object that uses Random Forest regression on sliding-window inputs.

It wraps the randomForest package to fit an ensemble of decision trees.

Usage

ts_rf(preprocess = NA, input_size = NA, nodesize = 1, ntree = 10, mtry = NULL)

Arguments

preprocess

Normalization preprocessor (e.g., ts_norm_gminmax()).

input_size

Integer. Number of lagged inputs used by the model.

nodesize

Integer. Minimum terminal node size.

ntree

Integer. Number of trees in the forest.

mtry

Integer. Number of variables randomly sampled at each split.

Details

Random Forests reduce variance by averaging many decorrelated trees. For tabular sliding-window features, they can capture nonlinearities and interactions without heavy feature engineering. Consider normalizing inputs for comparability across windows and tuning mtry, ntree, and nodesize.

Value

A ts_rf object (S3) inheriting from ts_regsw.

References

Examples

# Example: sliding-window Random Forest
# Load tools and data
library(daltoolbox)
data(tsd)

# Turn series into 10-lag windows and preview
ts <- ts_data(tsd$y, 10)
ts_head(ts, 3)

# Train/test split and (X, y) projection
samp <- ts_sample(ts, test_size = 5)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

# Define Random Forest and fit (tune ntree/mtry/nodesize as needed)
model <- ts_rf(ts_norm_gminmax(), input_size = 4, nodesize = 3, ntree = 50)
model <- fit(model, x = io_train$input, y = io_train$output)

# Forecast multiple steps and assess error
prediction <- predict(model, x = io_test$input[1,], steps_ahead = 5)
prediction <- as.vector(prediction)
output <- as.vector(io_test$output)

ev_test <- evaluate(model, output, prediction)
ev_test

Time Series Sample

Description

Split a ts_data into train and test sets.

Extracts test_size rows from the end (minus an optional offset) as the test set. The remaining initial rows form the training set. The offset is useful to reproduce experiments with different forecast origins.

Usage

ts_sample(ts, test_size = 1, offset = 0)

Arguments

ts

A ts_data matrix.

test_size

Integer. Number of rows in the test split (default = 1).

offset

Integer. Offset from the end before the test split (default = 0).

Value

A list with ⁠$train⁠ and ⁠$test⁠ (both ts_data).

Examples

# Setting up a ts_data and making a temporal split
# Load example dataset and build windows
data(tsd)
ts <- ts_data(tsd$y, 10)

# Separating into train and test
test_size <- 3
samp <- ts_sample(ts, test_size)

# First five rows from training data
ts_head(samp$train, 5)

# Last five rows from training data
ts_head(samp$train[-c(1:(nrow(samp$train)-5)),])

# Testing data
ts_head(samp$test)

SVM

Description

Create a time series prediction object that uses Support Vector Regression (SVR) on sliding-window inputs.

It wraps the e1071 package to fit epsilon-insensitive regression with linear, radial, polynomial, or sigmoid kernels.

Usage

ts_svm(
  preprocess = NA,
  input_size = NA,
  kernel = "radial",
  epsilon = 0,
  cost = 10
)

Arguments

preprocess

Normalization preprocessor (e.g., ts_norm_gminmax()).

input_size

Integer. Number of lagged inputs used by the model.

kernel

Character. One of 'linear', 'radial', 'polynomial', 'sigmoid'.

epsilon

Numeric. Epsilon-insensitive loss width.

cost

Numeric. Regularization parameter controlling margin violations.

Details

SVR aims to find a function with at most epsilon deviation from each training point while being as flat as possible. The cost parameter controls the trade-off between margin width and violations; epsilon controls the insensitivity tube width. RBF kernels often work well for nonlinear series; tune cost, epsilon, and kernel hyperparameters.

Value

A ts_svm object (S3) inheriting from ts_regsw.

References

Examples

# Example: SVR with min–max normalization
# Load package and dataset
library(daltoolbox)
data(tsd)

# Create sliding windows and preview
ts <- ts_data(tsd$y, 10)
ts_head(ts, 3)

# Temporal split and (X, y) projection
samp <- ts_sample(ts, test_size = 5)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

# Define SVM regressor and fit to training data
model <- ts_svm(ts_norm_gminmax(), input_size = 4)
model <- fit(model, x = io_train$input, y = io_train$output)

# Multi-step forecast and evaluation
prediction <- predict(model, x = io_test$input[1,], steps_ahead = 5)
prediction <- as.vector(prediction)
output <- as.vector(io_test$output)

ev_test <- evaluate(model, output, prediction)
ev_test

Time Series Tune

Description

Create a ts_tune object for hyperparameter tuning of a time series model.

Sets up a cross-validated search over hyperparameter ranges and input sizes for a base model. Results include the evaluated configurations and the selected best configuration.

Usage

ts_tune(input_size, base_model, folds = 10, ranges = NULL)

Arguments

input_size

Integer vector. Candidate input window sizes.

base_model

Base model object to tune (e.g., ts_mlp()).

folds

Integer. Number of cross-validation folds.

ranges

Named list of hyperparameter ranges to explore.

Value

A ts_tune object.

References

Examples

# Example: grid search over input_size and ELM hyperparameters
# Load library and example data
library(daltoolbox)
data(tsd)

# Prepare 10-lag windows and split into train/test
ts <- ts_data(tsd$y, 10)
ts_head(ts, 3)
samp <- ts_sample(ts, test_size = 5)
io_train <- ts_projection(samp$train)
io_test <- ts_projection(samp$test)

# Define tuning: vary input_size and ELM hyperparameters (nhid, actfun)
tune <- ts_tune(
  input_size = 3:5,
  base_model = ts_elm(ts_norm_gminmax()),
  ranges = list(nhid = 1:5, actfun = c('purelin'))
)

# Run CV-based search and get the best fitted model
model <- fit(tune, x = io_train$input, y = io_train$output)

# Forecast and evaluate on the held-out horizon
prediction <- predict(model, x = io_test$input[1,], steps_ahead = 5)
prediction <- as.vector(prediction)
output <- as.vector(io_test$output)

ev_test <- evaluate(model, output, prediction)
ev_test

Time series example dataset

Description

Synthetic dataset based on a sine function.

Usage

data(tsd)

Format

data.frame.

Source

This dataset was generated for examples.

Examples

# Load dataset and preview the first rows
data(tsd)
head(tsd)