TSdeeplearning - Deep Learning Model for Time Series Forecasting
RNNs are preferred for sequential data like time series,
speech, text, etc. but when dealing with long range
dependencies, vanishing gradient problems account for their
poor performance. LSTM and GRU are effective solutions which
are nothing but RNN networks with the abilities of learning
both short-term and long-term dependencies. Their structural
makeup enables them to remember information for a long period
without any difficulty. LSTM consists of one cell state and
three gates, namely, forget gate, input gate and output gate
whereas GRU comprises only two gates, namely, reset gate and
update gate. This package consists of three different functions
for the application of RNN, LSTM and GRU to any time series
data for its forecasting. For method details see Jaiswal, R. et
al. (2022). <doi:10.1007/s00521-021-06621-3>.
