Multivariate time series

Recurrent neural networks (RNNs) are deep learning models, typically used to solve problems with sequential input data such as time series .31 Eki 2021

There are two main approaches to time series forecasting – statistical approaches and neural network models. The most popular statistical method for time series forecasting is the ARIMA (Autoregressive Integrated Moving Average) family with AR, MA, ARMA, ARIMA, ARIMAX, and SARIMAX methods .

Neural networks have been successfully used for forecasting of financial data series . The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. Neural Networks have the advantage that can approximate nonlinear functions.

Recurrent Neural Networks are the most popular Deep Learning technique for Time Series Forecasting since they allow to make reliable predictions on time series in many different problems. The main problem with RNNs is that they suffer from the vanishing gradient problem when applied to long sequences.2 Kas 2020