Here you’ll find everything about recurrent neural networks, whether it’s articles describing how RNNs work, or Python tutorials showing how to implement them for stock market forecasting.
A recurrent neural network (RNN) is a type of neural network that is designed to process sequential data, such as time series data or natural language. Unlike traditional feedforward neural networks, which process inputs and produce outputs in a single pass, RNNs have the ability to process inputs that are ordered in time and to maintain an internal state that captures information about past inputs. 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.
Multi-output time series regression can forecast several steps of a time series at once. The number of neurons in the final output layer determines how many steps the model can predict. Models with one output return single-step forecasts. Models with various outputs can return entire series of time steps and … Read more
Multivariate forecasting models rely not 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 data … 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. It consists of multiple layers of long-term, short-term memory (LSTM). These LSTM layers allow the … Read more
Evaluating performance is a crucial step in developing regression models. Because regression models return continuous outputs, such models allow for different gradations of right or wrong. Therefore, we measure the deviation between predictions and actual values in numerical terms. However, a universal metric to measure the performance of regression models … Read more
Many time forecasting problems can be solved by predicting just one step into the future. However, some problems require a forecast for an extended period of time, which calls for a multi-step time series forecasting approach. This approach involves modeling the distribution of future values of a signal over a … Read more