New method of precipitation forecast model and validation

Abstract

There is a lot of time series data in many realistic sectors with different forecast techniques over the years. However, there is no unanimous conclusion on forecast techniques such as individual forecast models Autoregressive, Moving Averages, Autoregressive Moving Averages, Autoregressive Integrated Moving Averages, Artificial Neural Network, Long Short Term Memory Network and Auto-Regressive Conditional Heteroscedasticity / Generalized Autoregressive Conditional Heteroscedasticity and combination of forecast (Simple Average of forecasts, Minimum Variance Method and Regression Method of the combine). The most empirical hydrological time series models do not accurately forecast the weather. This paper focuses on a comparative study of different existing individual and combination forecasts with the proposed Hybrid Stochastic Model (HSM) forecast procedure. For this we consider a hydrological time series data of the Indian subcontinent to test the proposed forecast model. As a whole in comparison to all other traditional model's contributions accuracy, the proposed model performed well and also we examined the model's dimension reduction approach to choose an optimum number of forecast techniques to be included in the model to yield the best forecasts.