Forecasting non-linear macroeconomic indexes of India: an ensemble of MLP and Holt’s linear methods

Abstract

Electricity and electric-equipment play a critical role in modern living, and precise price forecasting for these items aids decision-makers in anticipating changes, planning, and budgeting ahead of time. This present research focused on the wholesale price indexes of items from the "manufacture of electrical equipment (MEEQ)" group of India's existing wholesale price index (WPI) series. This work proposed a novel, state-of-art ensemble forecast approach that used multilayer perceptron (MLP) and Holt's linear (HL) approaches for some specified WPIs from this group. The researchers selected the WPIs that manifest non-linearity as determined by the curve-fit technique. The paper applied a variance-based weighted average scheme to generate the ensemble forecast. The statistical rigor-based curve-fit aided in identifying that seventeen out of forty-eight indexes manifest non-linear fits. The proposed MLP-HL ensemble approach exhibited excellent forecast results (relative root mean squared error, i.e., RRMSE < 10%) for all seventeen WPIs. For each of these seventeen WPI's, the current work compared the proposed MLP-HL with nineteen models (eight statistical, four machine learning, and seven contemporary ensemble strategies). In the majority of the cases, it outran the nineteen models in terms of mean absolute error (MAE) and mean absolute percentage error (MAPE). As an alternative pathway for forecasting these seventeen non-linear WPIs, the present work suggests using this proposed MLP-HL approach.