Multivariate Time Series Prediction with LSTM and Multiple features (Predict Google Stock Price)

Build a Artificial Neural Network (ANN) with Long-Short Term Memory (LSTM) to predict value which can be impacted by multiple different features. In this video I demonstrate how to use LSTM to predict Google Stock price (you can use any other case) by taking into consideration multiple predictors (features). Let’s say, the final stock price can be predicted by finding importance of such features as historical low price, high price, volume, adj. price, etc. Link to github notebook: The video has 3 parts: - Part 01. Data pre-processing (4:11) | Step 01: Read data. | Step 02: Data pre-processing (shaping and transformations). - Part 02. Create a LSTM model and train it. (10:39) | Step 03: Building-up the LSTM based Neural Network. | Step 04: Start training. - Part 03. Make future predictions. (13:50) | Step 05: Make predictions for future date. | Step 06: Visualize the predictions. In this tutorial I used Tensorflow and Keras Download data from: (check 1:59 in video). This is real life Python code example for demonstration purposes, so the model is not very accuracy and of course could be improved or tuned. My goal of this Python tutorial is to demonstrate how to perform LSTM predictions with multiple features (complex dataset). Hoping it will help to undersant the way it could be implemented in real Data Science or Data Analysis projects. TIme Series forecasting with LSTM is the good choice if you want to manipulate with multiple different data features and see which ones has impact to predictions and which ones do not. If you are interested how to run Tensorboard on this LSTM Keras model, check this tutorial: Sorry for video quality. There were some unexpected issues with resolution. #lstm #timeseries #multivariate