| Type: | Package |
| Title: | PSO Based Weighted Ensemble Algorithm for Volatility Modelling |
| Version: | 0.1.0 |
| Author: | Mr. Ankit Kumar Singh [aut], Dr. Ranjit Kumar Paul [aut, cre], Dr. Amrit Kumar Paul [aut], Dr. Md Yeasin [aut], Ms. Anita Sarkar [aut] |
| Maintainer: | Dr. Ranjit Kumar Paul <ranjitstat@gmail.com> |
| Description: | Price volatility refers to the degree of variation in series over a certain period of time. This volatility is especially noticeable in agricultural commodities, adding uncertainty for farmers, traders, and others in the agricultural supply chain. Commonly and popularly used four volatility models viz, GARCH, Glosten Jagannatan Runkle-GARCH (GJR-GARCH) model, exponentially weighted moving average (EWMA) model and Multiplicative Error Model (MEM) are selected and implemented. PWAVE, weighted ensemble model based on particle swarm optimization (PSO) is proposed to combine the forecast obtained from all the candidate models. This package has been developed using algorithm of Paul et al. <doi:10.1007/s40009-023-01218-x> and Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | stats, xts, rumidas, rugarch, WeightedEnsemble, Metrics, zoo |
| RoxygenNote: | 7.2.1 |
| NeedsCompilation: | no |
| Packaged: | 2024-04-16 07:44:01 UTC; YEASIN |
| Repository: | CRAN |
| Date/Publication: | 2024-04-16 15:00:02 UTC |
PSO Based Weighted Ensemble Algorithm for Volatility Modelling
Description
PSO Based Weighted Ensemble Algorithm for Volatility Modelling
Usage
PWEV(Data, SplitR)
Arguments
Data |
Univariate Time Series Data |
SplitR |
Split Ratio |
Value
TrainFitted: Fitted values for the train series
TestPred: Prediction for the test series
Accuracy: Accuracy metric of the proposed model
Weights: Weights of the ensemble
References
Paul, R.K., Das, T. and Yeasin, M., 2023. Ensemble of time series and machine learning model for forecasting volatility in agricultural prices. National Academy Science Letters, 46(3), pp.185-188.
Yeasin, M. and Paul, R.K., 2024. OptiSembleForecasting: optimization-based ensemble forecasting using MCS algorithm and PCA-based error index. The Journal of Supercomputing, 80(2), pp.1568-1597.
Examples
library("PWEV")
data<- as.ts(rnorm(200,100,50))
Result <- PWEV(data, 0.9)