A Recurrent Neural Network (RNN) is a specific form of a Neural Network. In a traditional feed-forward Neural Network all the information flows from left to right. In contrast, RNNs use Long-short-term memory (LSTM)-layers that allow them to recirculate output results back and forth through the network. In other words, LSTM layers have loops that enable the RNN to pass output values from one training instance to iterative instances. This allows them to learn patterns that occur on different levels of abstraction.
RNNs are particularly useful for analyzing time series. In this application domain, the LSTM layers allow the RNN to learn patterns that occur over different time periods, e.g., days and months, and potentially overlap. As a result, RNNs typically generate more accurate predictions in time-series analysis than traditional neural networks.
Neural networks can generate various outputs, which is especially useful in time-series forecasting to forecast longer periods. In time series regression, the number of neurons in the final output layer determines how many steps in a time series the model can predict. Models with one output return single-step forecasts. Models … Read more
Multivariate forecasting models do not rely exclusively on historical time series data but use additional features (multivariate = multiple input variables) such as moving averages or momentum indicators. The underlying assumption is that various variables increase the accuracy of a forecast by helping the model identify patterns in the historical … Read more
Regression models based on recurrent neural networks (RNN) can recognize patterns in time series data, making them an exciting technology for stock market forecasting. What distinguishes these RNNs from traditional neural networks is their architecture, consisting of multiple layers of long-term, short-term memory (LSTM). The LSTM layers allow the model … Read more