Chowdari, K.K.Deb Barma, S.D.Bhat, N.Girisha, R.Gouda, K.C.2026-02-0620224th International Conference on Emerging Research in Electronics, Computer Science and Technology, ICERECT 2022, 2022, Vol., , p. -https://doi.org/10.1109/ICERECT56837.2022.10059717https://idr.nitk.ac.in/handle/123456789/29764This study evaluates ARIMA, Facebook Prophet and a new boosting algorithm framework known as ThymeBoost for time series prediction of monthly precipitation of Belagavi district (semi-arid) in Karnataka. The dataset was divided into three periods (1901-2002, 1951- 2002, and 1971 - 2002). The first 70% of the data for each period was applied for training while the rest for testing. Also, the datasets were used in two different forms for both training and testing. In the first set, raw data was used as it is, and the second set of data was used after normalizing the time series using the min-max concept (between 0 and 1). However, the normalized data were de-normalized for each period for performance metrics estimation. ThymeBoost is the best model for the first period of raw data and the second period of normalized data. In contrast, Prophet outperforms all other models for the normalized data in terms of all four measures. For the second period of raw data, no model emerged as the best model in terms of all performance metrics. Therefore, all three models performed similarly for the third period of raw and normalized data. © 2022 IEEE.ARIMAprecipitationProphetsemi-arid regionThymeBoost