WEATHER FORECASTING USING TIME SERIES MODELS
Keywords:
Weather Forecasting, Time Series Analysis, ARIMA, SARIMA, LSTM, Machine Learning, Weather Prediction, Data Preprocessing, Forecast Visualization, Crop Yield Prediction.Abstract
Weather forecasting plays a crucial role in agriculture, disaster management, transportation, and environmental planning. Accurate prediction of weather conditions such as temperature, rainfall, humidity, and wind speed helps individuals, farmers, and organizations make informed decisions and reduce risks caused by unexpected climate changes. This project presents a weather forecasting system using time series models to analyze historical weather data and predict future atmospheric conditions. Since weather data is collected sequentially over time, time series analysis is an effective approach for identifying patterns such as trends, seasonality, and cyclic variations. The system utilizes statistical models such as Autoregressive (AR), Moving Average (MA), Auto Regressive Integrated Moving Average (ARIMA), and Seasonal ARIMA (SARIMA), along with deep learning techniques like Long Short-Term Memory (LSTM) networks to improve prediction accuracy. The project involves multiple stages including data collection from reliable meteorological sources, data preprocessing to handle missing values and noise, feature engineering to extract meaningful weather indicators, and model training to learn patterns from historical data. The trained models generate forecasts for future weather conditions, which are then evaluated using performance metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The system also provides graphical visualization of predicted results to make the forecasts easy to understand for users. By integrating machine learning techniques with time series analysis, the proposed system improves forecasting accuracy and reliability compared to traditional methods. Overall, this project demonstrates how advanced predictive models can support better planning, improve agricultural productivity, and assist in environmental monitoring and disaster preparedness.
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Published
2026-03-15
How to Cite
Prof.S.V.C. GUPTA, KOMMANA DURGA LAKSHMI, JALLURI ESHA BHARGAVI, TIRUVEEDULA NAGA RUPA, GUNTUPALLI BALA SUBRAHMANYAM, & DOI: 10.48047/IJCNIS.18.3.50. (2026). WEATHER FORECASTING USING TIME SERIES MODELS. International Journal of Communication Networks and Information Security (IJCNIS), 18(3), 41–50. Retrieved from http://www.ijcnis.org/index.php/ijcnis/article/view/8868
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Section
Research Articles
