Stock Market Prediction using Neural Networks for Multi-Output Regression in Python

multi-output neural networks time series regression

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

Feature Engineering for Multivariate Stock Market Prediction with Python

feature engineering for stock market prediction, multivariate time series modelling

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

Stock Market Prediction using Multivariate Time Series and Recurrent Neural Networks in Python

stock market prediction Python tutorial

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