![]() Also, neural networks involve configuration of hyper-parameters and learning-rate etc., which are not straight-forward and need some iterations and fine tuning. But the cons with neural networks is that, you need a huge set of data to train a model unlike the classical methods which don’t need a large set of data.But deep neural networks are “magical” in the sense that they can learn the inherent patterns in different time series and come up with a sound model without the need for us to bother about breaking up the trend and seasonality patterns present in the time series data. In classical methods mentioned above, we must take care of pre-processing of the time series data like analyzing and removing the trend, seasonality etc., from the time series without which algorithms like ARIMA wouldn’t work.Deep Learning vs Classical Methods For Time Series Forecast They are used even today because of their effectiveness as well as in the cases where a large amount of data is not available that is essential to train RNNs. Time Series Forecast Using Deep Neural Networksīefore deep learning neural networks became popular, particularly the Recurrent Neural Networks, there were a number of classical analytical methods / algorithms used for Time Series forecast- AR, MA, ARMA, ARIMA, SARIMA etc. ![]()
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