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Particularly in areas with substantial climate variability, precise temperature forecasting is essential for energy management, agricultural planning, and disaster preparedness. The purpose of this study is to predict Uttar Pradesh's non-monsoon season temperature using two popular time series forecasting methods: Holt's Winter Smoothing Method and Simple Exponential Smoothing (SES). To forecast future temperature changes, the study applies both models to historical temperature data gathered over several years. While Holt's Winter approach incorporates both trend and seasonality in the data, SES is used to capture the underlying trend when seasonality is absent. Metrics like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used to assess both models' performance. The results indicate that Holt's Winter approach delivers better accuracy by accounting for seasonal variations, even if SES offers a dependable temperature forecast with less fluctuation. The study's conclusion emphasizes the importance of choosing the right model for precise long-term temperature predictions in areas with complex weather patterns and offers suggestions for the application of these models in climate-related forecasting in Uttar Pradesh, particularly during the non-monsoon season.
"Forecasting temperature using Simple exponential Smoothing and Holt’s Winter smoothing method in Uttar Pradesh", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 2, page no.a149-a153, February-2025, Available :http://www.ijrti.org/papers/IJRTI2502020.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator