article · Sustainability
Accurate temperature forecasting is an essential component across numerous environmental and operational applications. A hybrid statistical forecasting model has been established by combining Wavelet Decomposition with a Seasonal Auto-Regressive Integrated Moving Average with Exogenous Variables model, known as WD-SARIMAX. Tested on daily climate records from Delhi between 2013 and 2017 comprising 1,462 observations across four features, the method uses Wavelet Decomposition to separate non-stationary climate series into multi-dimensional components. This decomposition reduces the original volatility of the data, thereby increasing stability and predictability before passing the signals into the SARIMAX architecture. When evaluated on a twenty percent testing split, the hybrid approach achieved low error rates and a determination coefficient of 0.91, outperforming alternative recent models. The trained system was subsequently applied to project daily temperatures for Delhi over an eight-year horizon spanning 2017 to 2025.
Weather fluctuations introduce significant uncertainty into civic planning and environmental management. By combining signal decomposition with seasonal time series modelling, this methodology improves the precision of temperature predictions. Better predictive models help planners and technical analysts manage climate-sensitive activities with greater reliability, transforming volatile historical weather records into stable multi-year temperature forecasts.
The model demonstrates an applied and tested predictive tool suited for municipal bodies, meteorology services, and climate analytics providers seeking improved temperature forecasting. Although the technical framework has been verified on a five-year historical dataset, the abstract does not indicate whether it is packaged into deployable software or linked to an active operational pipeline, placing it at an applied research stage.
AI-generated from the published abstract. Always read the original work before citing.
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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.3390/su15010757
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
