Enhanced Short-term Temperature Prediction Through ARIMA-LSTM Hybrid Model

Abstract

Short-term temperature prediction plays a vital role in agriculture, water resource management, energy planning and climate change monitoring. Nevertheless, traditional statistical methods do not account for the nonlinearities in weather patterns, whereas deep learning models may not consider the linear time series characteristics. To solve this problem, the present study proposes a hybrid Auto-Regressive Integrated Moving Average-Long Short Term Memory (ARIMA-LSTM) model for short-term temperature prediction. The ARIMA model will be used in the proposed framework to identify the linear and seasonal components of temperature patterns. The errors produced by the ARIMA model will be further analyzed by means of an LSTM neural network that would model the non-linear time series behavior. The model is tested on the Tamil Nadu and Puducherry Weather Dataset (2022), consisting of roughly 1.8 million weather data sets, including variables like temperature of the air, humidity level, wind speed, and pressure. The evaluation criteria include mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The experimental findings reveal that the developed model performs better in terms of accuracy than traditional deep learning models, thus providing an effective and reliable tool for predicting temperature in short intervals.