Package: hybridts 0.1.0
Tanujit Chakraborty
hybridts: Hybrid Time Series Forecasting Using Error Remodeling Approach
Method and tool for generating hybrid time series forecasts using an error remodeling approach. These forecasting approaches utilize a recursive technique for modeling the linearity of the series using a linear method (e.g., ARIMA, Theta, etc.) and then models (forecasts) the residuals of the linear forecaster using non-linear neural networks (e.g., ANN, ARNN, etc.). The hybrid architectures comprise three steps: firstly, the linear patterns of the series are forecasted which are followed by an error re-modeling step, and finally, the forecasts from both the steps are combined to produce the final output. This method additionally provides the confidence intervals as needed. Ten different models can be implemented using this package. This package generates different types of hybrid error correction models for time series forecasting based on the algorithms by Zhang. (2003), Chakraborty et al. (2019), Chakraborty et al. (2020), Bhattacharyya et al. (2021), Chakraborty et al. (2022), and Bhattacharyya et al. (2022) <doi:10.1016/S0925-2312(01)00702-0> <doi:10.1016/j.physa.2019.121266> <doi:10.1016/j.chaos.2020.109850> <doi:10.1109/IJCNN52387.2021.9533747> <doi:10.1007/978-3-030-72834-2_29> <doi:10.1007/s11071-021-07099-3>.
Authors:
hybridts_0.1.0.tar.gz
hybridts_0.1.0.tar.gz(r-4.5-noble)hybridts_0.1.0.tar.gz(r-4.4-noble)
hybridts_0.1.0.tgz(r-4.4-emscripten)hybridts_0.1.0.tgz(r-4.3-emscripten)
hybridts.pdf |hybridts.html✨
hybridts/json (API)
# Install 'hybridts' in R: |
install.packages('hybridts', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:5eab896f11. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 17 2024 |
R-4.5-linux | OK | Dec 17 2024 |
Exports:arima_annarima_arnnarima_warimaets_arnnrw_annrw_arnnsummary_hybridtstheta_anntheta_arnnwarima_annwarima_arnn
Dependencies:askpassclicodetoolscolorspacecurlDerivfansifarverforeachforecastfracdiffgenericsggplot2glmnetgluegreyboxgtablehttrisobanditeratorsjsonlitelabelinglatticelifecyclelmtestmagrittrMAPAMASSMatrixMetricsmgcvmimemunsellneuralnetnlmenloptrnnetnnforopensslpillarpkgconfigplotrixpracmaquadprogquantmodR6RColorBrewerRcppRcppArmadilloRcppEigenrlangscalesshapesmoothstatmodsurvivalsystexregtibbletimeDatetseriestsutilsTTRurcaurootutf8vctrsviridisLiteWaveletArimawaveletswithrxtablextszoo
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Hybrid ARIMA ANN Forecasting Model | arima_ann |
Hybrid ARIMA ARNN Forecasting Model | arima_arnn |
Hybrid ARIMA WARIMA Forecasting Model | arima_warima |
Hybrid ETS ARNN Forecasting Model | ets_arnn |
Hybrid Random Walk ANN Forecasting Model | rw_ann |
Hybrid Random Walk ARNN Forecasting Model | rw_arnn |
Summarized score of all the hybrid models implemented in this package | summary_hybridts |
Hybrid Theta ANN Forecasting Model | theta_ann |
Hybrid Theta ARNN Forecasting Model | theta_arnn |
Hybrid WARIMA ANN Forecasting Model | warima_ann |
Hybrid WARIMA ARNN Forecasting Model | warima_arnn |