OptiSembleForecasting (version 0.1.0)

OptiSembleForcasting: OptiSembleForecasting

Description

Optimization Based Ensemble Forecasting Using MCS Algorithm

Usage

OptiSembleForcasting(TS, Lag, Optimization, Split_ratio)

Value

• SelectedModel: Selected models with weights

• Accuracy: Accuracy matrix

• TestResults: Final predicted value

Arguments

TS

Time series data with first column as date

Lag

Number of lag for modelling

Optimization

Optimization technique

Split_ratio

Train-Test Split Ration

References

• Wang, J., Wang, Y., Li, H., Yang, H. and Li, Z. (2022). Ensemble forecasting system based on decomposition-selection-optimization for point and interval carbon price prediction. Applied Mathematical Modelling, doi.org/10.1016/j.apm.2022.09.004.

• Qu, Z., Li, Y., Jiang, X. and Niu, C. (2022). An innovative ensemble model based on multiple neural networks and a novel heuristic optimization algorithm for COVID-19 forecasting. Expert System Application, doi:10.1016/j.eswa.2022.118746

• Kriz, K.A. (2019). Ensemble Forecasting. In: Williams, D., Calabrese, T. (eds) The Palgrave Handbook of Government Budget Forecasting. Palgrave Studies in Public Debt, Spending, and Revenue. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-18195-6_21

Examples

# \donttest{
library(OptiSembleForecasting)
date<-seq.Date(from = as.Date('2019-09-17'), to = as.Date('2022-09-18'), by = 'days')
value<-rnorm(length(date),100, 50)
data<-cbind(date,value)
fit<-OptiSembleForcasting(TS=data,Lag = 20, Optimization = "ABC",Split_ratio = 0.9)
# }