Package 'PWEV'

Title: PSO Based Weighted Ensemble Algorithm for Volatility Modelling
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>.
Authors: 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 <[email protected]>
License: GPL-3
Version: 0.1.0
Built: 2025-02-11 04:27:14 UTC
Source: https://github.com/cran/PWEV

Help Index

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)