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

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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

Measuring Regression Errors with Python

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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

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

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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

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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

Stock Market Prediction using Univariate Recurrent Neural Networks (RNN) with Python

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Predicting the price of financial assets has fascinated researchers and analysts for many decades. While the traditional prediction methods of technical analysis and fundamental analysis are still widely used, interest is increasingly turning to machine-generated predictions based on deep learning. A contributing factor is that libraries for deep learning, such … Read more