A Novel WD-SARIMAX Model for Temperature Forecasting Using Daily Delhi Climate Dataset

Abstract

Forecasting is defined as the process of estimating the change in uncertain situations. One of the most vital aspects of many applications is temperature forecasting. Using the Daily Delhi Climate Dataset, we utilize time series forecasting techniques to examine the predictability of temperature. In this paper, a hybrid forecasting model based on the combination of Wavelet Decomposition (WD) and Seasonal Auto-Regressive Integrated Moving Average with Exogenous Variables (SARIMAX) was created to accomplish accurate forecasting for the temperature in Delhi, India. The range of the dataset is from 2013 to 2017. It consists of 1462 instances and four features, and 80% of the data is used for training and 20% for testing. First, the WD decomposes the non-stationary data time series into multi-dimensional components. That can reduce the original time series’ volatility and increase its predictability and stability. After that, the multi-dimensional components are used as inputs for the SARIMAX model to forecast the temperature in Delhi City. The SARIMAX model employed in this work has the following order: (4, 0, 1). (4, 0, [1], 12). The experimental results demonstrated that WD-SARIMAX performs better than other recent models for forecasting the temperature in Delhi city. The Mean Square Error (MSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and determination coefficient (R2) of the proposed WD-SARIMAX model are 2.8, 1.13, 0.76, 1.67, 4.9, and 0.91, respectively. Furthermore, the WD-SARIMAX model utilized the proposed to forecast the temperature in Delhi over the next eight years, from 2017 to 2025.