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This study aims to check the predictive ability of ARIMA, LSTM, and Dense models on temperature. In terms of accuracy, LSTM models (accuracy 92.48% with Nadam optimizer) consistently outperformed Dense(accuracy 88.35%) and ARIMA models(accuracy 76.02%) in their ability to capture long-term dependencies and nonlinear interactions in tissme series data. While ARIMA models (accuracy 76.02%with RMSE value 0.0372) perform reasonably well on simpler patterns, they may not perform well on more complicated temperature patterns. The study provides insightful knowledge to enable the adoption of certain models for temperature prediction exercises and support resource management, as well as decision making. This research advances temperature forecasting methodology via the comparison of these models with important ramifications for energy management, agriculture, climate change research, and beyond.

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Harnessing the Power of LSTM, ARIMA, and Dense Models for Accurate Temperature Prediction with Time Series Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Durjoy Chatterjee,
  • Riyanka Das,
  • Deepsubhra Guha Roy,
  • Piyali Datta,
  • Dimitrios A. Karras

摘要

This study aims to check the predictive ability of ARIMA, LSTM, and Dense models on temperature. In terms of accuracy, LSTM models (accuracy 92.48% with Nadam optimizer) consistently outperformed Dense(accuracy 88.35%) and ARIMA models(accuracy 76.02%) in their ability to capture long-term dependencies and nonlinear interactions in tissme series data. While ARIMA models (accuracy 76.02%with RMSE value 0.0372) perform reasonably well on simpler patterns, they may not perform well on more complicated temperature patterns. The study provides insightful knowledge to enable the adoption of certain models for temperature prediction exercises and support resource management, as well as decision making. This research advances temperature forecasting methodology via the comparison of these models with important ramifications for energy management, agriculture, climate change research, and beyond.