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A deep-learning Vanilla, or single layer, Long Short-Term Memory model is proposed for improving the prediction of Solar Cycle 25. WDC-SILSO the Royal Observatory of Belgium, Brussels provides the 13-month smoothed sunspot-number data that were used to make this prediction. The root mean square error (RMSE) obtained by the proposed model, which is improved in comparison to the existing stacked LSTM model, lies within the range of 1.65 – 4.92, according to analysis on a number of temporal intervals taken into consideration in this study. The model performance has been validated by forecasting the peak amplitude of Solar Cycles 21 – 24. It is shown that for Cycles 21 and 22, the prediction error in estimating the peak is 1.159% and 0.423%, while the RMSE is estimated to be 4.149 and 3.274, respectively. For Cycle 23, the relative error and RMSE are 1.054% and 2.985, respectively, whereas for Cycle 24 they are 1.117% and 3.406, respectively. The current proposed model has exactly predicted the timing when the SSN reached its maximum for Cycle 23. While for Cycle 21, the prediction has a 1-month delay from the actual timing. For Cycles 22 and 24, the year during which the SSN reached maximum coincides with the observed year, although their month of peak occurrence showed a difference of three months and one month, respectively. The current proposed model suggests that the Cycle 25 will peak in April 2023 with an amplitude value of 136.9, which will be approximately 17.68% stronger compared with Cycle 24. [ABSTRACT FROM AUTHOR]

• Peak & timing of solar cycle 25 has been predicted using sunspot number data. • The concept of Deep Learning algorithm has been used for forecasting. • The proposed model showed good performance on various time intervals with low RMSE value. • Validation of model performance has been done by predicting past solar cycles 20–24. • The model captured non-linearity and cyclic trend within the data with good RMSE. In the current work we have used the deep learning based long short-term memory model to predict the strength and peak time of solar cycle 25 by employing the monthly smoothed sunspot number data obtained from WDC-SILSO, Royal Observatory of Belgium, Brussels. We have used the stacked LSTM forecasting model to predict the upcoming cycle 25. From our analysis it has been shown that our proposed model is capable of capturing long term dependencies as well as trend within the data. For cycle 20 and 21 the error difference between predicted as well as observed peak value is 2.3 and 0.7 respectively while the peak prediction error is 1.47% and 0.30%. The RMSE of the model for cycle 20 and 21 is 3.97 and 4.34 respectively. For cycle 22, the AE and RE is 4.6 and 2.16% while the RMSE of the model for this case is 4.50. The predicted peak amplitude of solar cycle 23 and 24 from our proposed model has a relative error of 1.75% and 1.99% respectively from the observed value while the RMSE is 3.4 for cycle 23 and 4.2 for cycle 24. Our projected prediction of cycle 25 using the proposed LSTM model, says that it will be stronger than cycle 24 and weaker than cycle 23. The solar cycle 25 will peak with an amplitude of sunspot number at 171.9 ± 3.4 and will be 47 % stronger than cycle 24. The solar cycle 25 will reach its peak in August 2023 ± 2 months. [ABSTRACT FROM AUTHOR]