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

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