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 |
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 |
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 |
CATS | Time series of the CATS Competition |
CATS.cont | Continuation dataset of the time series of the CATS Competition |
detrend | Detrending Transformation |
detrend.rev | Detrending Transformation |
DIFF | Time series transformation methods |
Diff | Differencing Transformation |
Diff.rev | Differencing Transformation |
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 |
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 |
HW | Time series prediction models |
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) |
linear | Time series modeling and prediction |
LogLik_eval | Prediction/modeling quality metrics |
LogT | Logarithmic Transformation |
LogT.rev | Logarithmic Transformation |
LT | Time series transformation methods |
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 |
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 |
outliers_bp | Outlier removal from sliding windows of data |
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 |
RBF | Time series prediction models |
RFrst | Time series prediction models |
RMSE_eval | Prediction/modeling quality metrics |
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 |
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 |
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 |