OptiSembleForcasting {OptiSembleForecasting} | R Documentation |
OptiSembleForecasting
Description
Optimization Based Ensemble Forecasting Using MCS Algorithm
Usage
OptiSembleForcasting(TS, Lag, Optimization, Split_ratio)
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 |
Value
SelectedModel: Selected models with weights
Accuracy: Accuracy matrix
TestResults: Final predicted value
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
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)