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 |