Time series forecasting for uni- variant data using hybrid GA-OLSTM model and performance evaluations.

Abstract

Time series forecasting of uni-variant rainfall data is done using a hybrid genetic algorithm integrated with optimized long-short term memory (GA-OLSTM) model. The parameters included for the valuation of the efficiency of the considered model, were mean square error (MSE), root mean square error (RMSE), cosine similarity (CS) and correlation coefficient (r). With various epochs like 5, 10, 15 and 20, the optimal window size and the number of units were observed using the GA search algorithm which was found to be (49, 9), (12, 8), (40, 8), and (36, 2) respectively. The computed MSE, RMSE, CS and r for 10 epochs were found to be 0.006, 0.078, 0.910 and 0.858 respectively for the LSTM model, whereas the same parameters were computed using the Hybrid GA-OLSTM model was 0.004, 0.063, 0.947 and 0.917 respectively. The experimental results expressed that the Hybrid GA-OLSTM model gave significantly better results comparing the LSTM model for 10 epochs has been discussed in this research article.