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TSPred

TSPred Package for R : Framework for Nonstationary Time Series Prediction

https://github.com/rebeccasalles/tspred

Science Score: 10.0%

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Keywords

benchmarking linear-models machine-learning nonstationarity time-series-forecast time-series-prediction
Last synced: 6 months ago · JSON representation

Repository

TSPred Package for R : Framework for Nonstationary Time Series Prediction

Basic Info
  • Host: GitHub
  • Owner: RebeccaSalles
  • License: gpl-3.0
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 478 MB
Statistics
  • Stars: 25
  • Watchers: 6
  • Forks: 8
  • Open Issues: 1
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Topics
benchmarking linear-models machine-learning nonstationarity time-series-forecast time-series-prediction
Created about 9 years ago · Last pushed 9 months ago
Metadata Files
Readme License

README.md

codecov cran version downloads Research software impact

TSPred Package for R : Framework for Nonstationary Time Series Prediction

Current Version: 5.1 Date: 2021-01

Authors: Rebecca Pontes Salles (rebeccapsalles@acm.org) and Eduardo Ogasawara (eogasawara@ieee.org)

Description: Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modeling, prediction, and accuracy assessment. The generated models and their yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.

Available at CRAN: https://CRAN.R-project.org/package=TSPred

Reference manual: TSPred.pdf

Acknowledgements: The authors thank CNPq, CAPES, and FAPERJ for partially sponsoring this research.

Usage:

```r

Install TSPred package

install.packages("TSPred")

Load TSPred package

library("TSPred") ```

ARIMA model prediction application using TSPred

```r

loading CATS dataset

data("CATS")

defining the time series application

tspredarima <- tspred( subsetting = subsetting(testlen = 20), modeling = ARIMA(), evaluating = list(MSE = MSE(),AIC = AIC()) )

performing the prediction application and obtaining results

tspredarimares <- workflow( tspred_arima, data = CATS[5] ) ```

Definition of components/steps of a time series prediction process in TSPred

```r

Obtaining objects of the processing class

procsubset <- subsetting( testlen = 20 ) procbct <- BCT() procwt <- WT( level = 1, filter = "bl14" ) procsw <- SW( windowlen = 6 ) proc_mm <- MinMax()

Obtaining objects of the modeling class

modlnnet <- NNET( size = 5, sw = procsw, proc = list(MM = proc_mm) )

Obtaining objects of the evaluating class

eval_mse <- MSE() ```

MLM prediction application using TSPred

```r

Defining a time series prediction process

tspredmlm <- tspred( subsetting = procsubset, processing = list(BCT = procbct, WT = procwt), modeling = modlnnet, evaluating = list(MSE = evalmse) )

Running the time series prediction process and obtaining results

tspredmlmres <- tspredmlm %>% subset(data = CATS[5]) %>% preprocess(preptest = TRUE) %>% train() %>% predict(inputtestdata = TRUE) %>% postprocess() %>% evaluate()

Benchmarking tspred objects

bmrkresults <- benchmark( tspredarimares, list(tspredmlm_res) ) ```

A user-defined MLM using TSPred

```r

Subclass my.model

my.model <- function(trainpar=NULL, predpar=NULL){ MLM(trainfunc = my.model.func, trainpar = c(trainpar), predfunc = my.model.pred.func, predpar = c(predpar), method = "Name of my model", subclass = "my.model" ) }

Obtaining an instance of the subclass my.model

model <- my.model(trainpar = list(par1="a", par2="b"), predpar = list(par3="c")) ```

Other relevant functions:

Nonstationarity treatment:

  • LogT - Logarithmic transform.
  • BCT - Box-Cox transform.
  • an - Adaptive normalization.
  • Diff - Differencing. MAS
  • mas - Moving average smoothing.
  • pct - Percentage change transform.
  • WaveletT - Wavelet transform.
  • emd - Empirical mode decomposition. #### Fittest linear models:
  • fittestLM - Automatically finding fittest linear model for prediction.
  • fittestArima - Automatic ARIMA fitting, prediction and accuracy evaluation.
  • fittestArimaKF - Automatic ARIMA fitting and prediction with Kalman filter.
  • fittestPolyR - Automatic fitting and prediction of polynomial regression.
  • fittestPolyRKF - Automatic fitting and prediction of polynomial regression with Kalman filter. #### Automatic preprocessing/decomposition and prediction:
  • fittestMAS - Automatic prediction with moving average smoothing.
  • fittestWavelet - Automatic prediction with wavelet transform.
  • fittestEMD - Automatic prediction with empirical mode decomposition.

Owner

  • Name: Rebecca Pontes Salles
  • Login: RebeccaSalles
  • Kind: user

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Top Committers
Name Email Commits
Rebecca Pontes Salles r****s@g****m 154
Eduardo Ogasawara e****a@e****r 1
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eic.cefet-rj.br: 1

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Packages

  • Total packages: 1
  • Total downloads:
    • cran 804 last-month
  • Total dependent packages: 2
  • Total dependent repositories: 1
  • Total versions: 9
  • Total maintainers: 1
cran.r-project.org: TSPred

Functions for Benchmarking Time Series Prediction

  • Versions: 9
  • Dependent Packages: 2
  • Dependent Repositories: 1
  • Downloads: 804 Last month
Rankings
Forks count: 8.0%
Stargazers count: 11.2%
Dependent packages count: 13.6%
Average: 15.4%
Downloads: 20.6%
Dependent repos count: 23.8%
Maintainers (1)
rebeccapsalles@acm.org
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.5.0 depends
  • EMD * imports
  • KFAS * imports
  • ModelMetrics * imports
  • MuMIn * imports
  • RSNNS * imports
  • Rlibeemd * imports
  • dplyr * imports
  • e1071 * imports
  • elmNNRcpp * imports
  • forecast * imports
  • keras * imports
  • magrittr * imports
  • methods * imports
  • nnet * imports
  • plyr * imports
  • randomForest * imports
  • stats * imports
  • tfdatasets * imports
  • vars * imports
  • wavelets * imports