Why is LSTM model better?

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.

LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series . LSTMs were developed to deal with the vanishing gradient problem that can be encountered when training traditional RNNs.

LSTM are useful for making predictions, classification and processing sequential data . We use many kinds of LSTM for different purposes or for different specific types of time series forecasting.

We see that ARIMA yields the best performance, i.e. it achieves the smallest mean square error and mean absolute error on the test set . In contrast, the LSTM neural network performs the worst of the three models. The exact predictions plotted against the true values can be seen in the following images.4 Oca 2022