An Iterative Model on ARIMA with LSTM Approach on Weather Forecasting
An Iterative Model on ARIMA with LSTM Approach on Weather Forecasting |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-10 |
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| Year of Publication : 2024 | ||
| Author : K. Sowjanya Bharathi, Boddu Sekhar Babu |
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| DOI : 10.14445/22315381/IJETT-V72I10P111 | ||
How to Cite?
K. Sowjanya Bharathi, Boddu Sekhar Babu, "An Iterative Model on ARIMA with LSTM Approach on Weather Forecasting," International Journal of Engineering Trends and Technology, vol. 72, no. 10, pp. 96-118, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I10P111
Abstract
Weather forecasting is essential for everyday living, influencing several industries and decision-making processes. This study looks at two ways to make short-term weather predictions better: the AutoRegressive Integrated Moving Average (ARIMA) model and a new method that uses Local Mean Squared Error (LMSE) optimisation in Long Short-Term Memory (LSTM) neural networks. By using several weather datasets, such as the UCI weather dataset, we may identify critical trends in meteorological data. The first segment of the proposed work implements the ARIMA model to examine historical weather data, isolating essential autoregressive and moving average elements for precise short-term forecasts. The following section presents LMSE optimisation inside LSTM networks, which refines the model to minimise prediction errors and enhance comprehension of long-term relationships in the data. This study aims to enhance the precision of short-term weather predictions by integrating the advantages of ARIMA and LMSE-optimised LSTM models. The results will provide meteorologists with dependable instruments for forecasting meteorological phenomena, assist emergency responders in making educated choices, and deliver more precise weather data to the public, improving readiness and safety in the face of fluctuating weather conditions.
Keywords
ARIMA, AdaSTL, CNN, GRU, LSTM, LMSE, Short-term forecasts, Time series analysis, UCI weather dataset, Weather forecasting.
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