Meteorological time series forecasting based on MLP modelling using heterogeneous transfer functions

TL;DR

This paper introduces a novel MLP-based approach with heterogeneous transfer functions and temporal indicators to improve meteorological time series forecasting accuracy over traditional methods.

Contribution

It presents a new MLP modeling technique combining different transfer functions and temporal inputs to better capture seasonal patterns in meteorological data.

Findings

Improved forecasting accuracy over classical MLP models.

Effective modeling of seasonal patterns with heterogeneous transfer functions.

Validated on two years of measurement data.

Abstract

In this paper, we propose to study four meteorological and seasonal time series coupled with a multi-layer perceptron (MLP) modeling. We chose to combine two transfer functions for the nodes of the hidden layer, and to use a temporal indicator (time index as input) in order to take into account the seasonal aspect of the studied time series. The results of the prediction concern two years of measurements and the learning step, eight independent years. We show that this methodology can improve the accuracy of meteorological data estimation compared to a classical MLP modelling with a homogenous transfer function.