R: Functions for Benchmarking Time Series Prediction

# Functions for Benchmarking Time Series Prediction

## Documentation for package ‘TSPred’ version 5.1.1

- DESCRIPTION file

## Help Pages

A B C D E F H I L M N O P R S T W

TSPred-package | Functions for Benchmarking Time Series Prediction

## -- A --

AICc_eval | Prediction/modeling quality metrics
AIC_eval | Prediction/modeling quality metrics
AN | Time series transformation methods
an | Adaptive Normalization
an.rev | Adaptive Normalization
ARIMA | Time series prediction models
arimainterp | Interpolation of unknown values using automatic ARIMA fitting and prediction
arimaparameters | Get ARIMA model parameters
arimapred | Automatic ARIMA fitting and prediction

## -- B --

BCT | Box Cox Transformation
BCT.rev | Box Cox Transformation
benchmark | Benchmarking a time series prediction process
benchmark.tspred | Benchmarking a time series prediction process
BIC_eval | Prediction/modeling quality metrics
BoxCoxT | Time series transformation methods

## -- C --

CATS | Time series of the CATS Competition
CATS.cont | Continuation dataset of the time series of the CATS Competition

## -- D --

detrend | Detrending Transformation
detrend.rev | Detrending Transformation
DIFF | Time series transformation methods
Diff | Differencing Transformation
Diff.rev | Differencing Transformation

## -- E --

ELM | Time series prediction models
EMD | Time series transformation methods
emd | Automatic empirical mode decomposition
emd.rev | Automatic empirical mode decomposition
error | Prediction/modeling quality evaluation
ETS | Time series prediction models
EUNITE.Loads | Electrical loads of the EUNITE Competition
EUNITE.Loads.cont | Continuation dataset of the electrical loads of the EUNITE Competition
EUNITE.Reg | Electrical loads regressors of the EUNITE Competition
EUNITE.Reg.cont | Continuation dataset of the electrical loads regressors of the EUNITE Competition
EUNITE.Temp | Temperatures of the EUNITE Competition
EUNITE.Temp.cont | Continuation dataset of the temperatures of the EUNITE Competition
evaluate | Evaluating prediction/modeling quality
evaluate.error | Evaluating prediction/modeling quality
evaluate.evaluating | Evaluating prediction/modeling quality
evaluate.fitness | Evaluating prediction/modeling quality
evaluate.tspred | Evaluate method for 'tspred' objects
evaluating | Prediction/modeling quality evaluation

## -- F --

fitness | Prediction/modeling quality evaluation
fittestArima | Automatic ARIMA fitting, prediction and accuracy evaluation
fittestArimaKF | Automatic ARIMA fitting and prediction with Kalman filter
fittestEMD | Automatic prediction with empirical mode decomposition
fittestLM | Automatically finding fittest linear model for prediction
fittestMAS | Automatic prediction with moving average smoothing
fittestPolyR | Automatic fitting and prediction of polynomial regression
fittestPolyRKF | Automatic fitting and prediction of polynomial regression with Kalman filter
fittestWavelet | Automatic prediction with wavelet transform

## -- H --

HW | Time series prediction models

## -- I --

ipeadata_d | The Ipea Most Requested Dataset (daily)
ipeadata_d.cont | The Ipea Most Requested Dataset (daily)
ipeadata_m | The Ipea Most Requested Dataset (monthly)
ipeadata_m.cont | The Ipea Most Requested Dataset (monthly)

## -- L --

linear | Time series modeling and prediction
LogLik_eval | Prediction/modeling quality metrics
LogT | Logarithmic Transformation
LogT.rev | Logarithmic Transformation
LT | Time series transformation methods

## -- M --

MAPE | MAPE error of prediction
MAPE_eval | Prediction/modeling quality metrics
marimapar | Get parameters of multiple ARIMA models.
marimapred | Multiple time series automatic ARIMA fitting and prediction
MAS | Time series transformation methods
mas | Moving average smoothing
mas.rev | Moving average smoothing
MAXError | Maximal error of prediction
MAXError_eval | Prediction/modeling quality metrics
MinMax | Time series transformation methods
minmax | Minmax Data Normalization
minmax.rev | Minmax Data Normalization
MLM | Time series modeling and prediction
MLP | Time series prediction models
modeling | Time series modeling and prediction
MSE | MSE error of prediction
MSE_eval | Prediction/modeling quality metrics

## -- N --

NAS | Time series transformation methods
NMSE | NMSE error of prediction
NMSE_eval | Prediction/modeling quality metrics
NN3.A | Dataset A of the NN3 Competition
NN3.A.cont | Continuation dataset of the Dataset A of the NN3 Competition
NN5.A | Dataset A of the NN5 Competition
NN5.A.cont | Continuation dataset of the Dataset A of the NN5 Competition
NNET | Time series prediction models

## -- O --

outliers_bp | Outlier removal from sliding windows of data

## -- P --

PCT | Time series transformation methods
pct | Percentage Change Transformation
pct.rev | Percentage Change Transformation
plotarimapred | Plot ARIMA predictions against actual values
postprocess | Preprocessing/Postprocessing time series data
postprocess.processing | Preprocessing/Postprocessing time series data
postprocess.tspred | Postprocess method for 'tspred' objects
predict | Predict method for 'modeling' objects
predict.linear | Predict method for 'modeling' objects
predict.MLM | Predict method for 'modeling' objects
predict.tspred | Predict method for 'tspred' objects
preprocess | Preprocessing/Postprocessing time series data
preprocess.processing | Preprocessing/Postprocessing time series data
preprocess.tspred | Preprocess method for 'tspred' objects
processing | Time series data processing

## -- R --

RBF | Time series prediction models
RFrst | Time series prediction models
RMSE_eval | Prediction/modeling quality metrics

## -- S --

SantaFe.A | Time series A of the Santa Fe Time Series Competition
SantaFe.A.cont | Continuation dataset of the time series A of the Santa Fe Time Series Competition
SantaFe.D | Time series D of the Santa Fe Time Series Competition
SantaFe.D.cont | Continuation dataset of the time series D of the Santa Fe Time Series Competition
sMAPE | sMAPE error of prediction
sMAPE_eval | Prediction/modeling quality metrics
subset | Subsetting data into training and testing sets
subset.tspred | Subsetting data into training and testing sets
subsetting | Time series transformation methods
SVM | Time series prediction models
SW | Time series transformation methods
sw | Generating sliding windows of data

## -- T --

Tensor_CNN | Time series prediction models
Tensor_LSTM | Time series prediction models
TF | Time series prediction models
train | Training a time series model
train.linear | Training a time series model
train.MLM | Training a time series model
train.tspred | Train method for 'tspred' objects
train_test_subset | Get training and testing subsets of data
tspred | Time series prediction process

## -- W --

WaveletT | Automatic wavelet transform
WaveletT.rev | Automatic wavelet transform
workflow | Executing a time series prediction process
workflow.tspred | Executing a time series prediction process
WT | Time series transformation methods