LSTM time series Python

LSTM stands for Long short-term memory . LSTM cells are used in recurrent neural networks that learn to predict the future from sequences of variable lengths. Note that recurrent neural networks work with any kind of sequential data and, unlike ARIMA and Prophet, are not restricted to time series.4 Oca 2022

Using LSTM, time series forecasting models can predict future values based on previous, sequential data . This provides greater accuracy for demand forecasters which results in better decision making for the business.

– Compare the performance of LSTM and ARIMA with respect to minimization achieved in the error rates in prediction. The study shows that LSTM outperforms ARIMA . The average reduction in error rates obtained by LSTM is between 84 – 87 percent when compared to ARIMA indicating the superiority of LSTM.