Recurrent Neural Networks

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

multi-output neural networks time series regression

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. We can design neural network architectures with multiple outputs in time-series forecasting to forecast more extended periods. Models with one output return single-step forecasts. Models … 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

Rolling Time Series Forecasting: Creating a Multi-Step Prediction for a Rising Sine Curve using Neural Networks in Python

adjusting time series intervals python

We can solve many time forecasting problems by looking at a single step into the future. However, some forecasting problems require us to understand how a signal will develop over a more extended period. Such cases require a multi-step time series forecasting approach that generates a forecast for multiple time … Read more

Stock Market Prediction – Adjusting Time Series Prediction Intervals in Python

neural networks time series regression stock market prediction python tutorial keras tensorflow

This tutorial shows how to adjust prediction intervals in time series forecasting using Keras recurrent neural networks and Python. We build on a previous article on stock market forecasting, in which we created a forecast for the S&P500 stock market index. The prediction interval used in this previous article was … Read more