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Rainfall is an essential hydrological component, and most of the economicactivities of an agrarian country like Bangladesh depend on rainfall. Anaccurate rainfall forecast can help make necessary decisions and reduce thedamages caused by heavy or low to no rainfall. The monthly average rainfall isa time series data, and recently, long short-term memory (LSTM) neural networksare being used heavily for time series forecasting problems. One majorchallenge of forecasting using LSTMs is to select the appropriate number of lagvalues. In this research, we considered the number of lag values selected as ahyperparameter of LSTM; it, with the other hyperparameters determining LSTMsstructure, has been optimized using Bayesian optimization. We used our proposedmethod to forecast rainfall for nine different weather stations of Bangladesh.Finally, the performance of the proposed model has been compared with someother LSTM with different lag-selection methods and some several popularmachine learning and statistical forecasting models.

Automated lag-selection for multi-step univariate time series forecast  using Bayesian Optimization: Forecast station-wise monthly rainfall of nine  divisional cities of Bangladesh