Stock Market Analysis with RNN - LSTM

Using RNN - LSTM and Time Series to predict Google stock price

• The project overview
  • Data Preprocessing
  • Modeling
  • Evaluation

Example result

What is RNN?

Recurrent Neural Networks can Memorize/remember previous inputs in-memory When a huge set of Sequential data is given to it.

These loops make recurrent neural networks seem kind of mysterious. However, if you think a bit more, it turns out that they aren’t all that different than a normal neural network. A recurrent neural network can be thought of as multiple copies of the same network, each passing a message to a successor.

Different types of Recurrent Neural Networks.

• Image Classification
• Sequence output (e.g. image captioning takes an image and outputs a sentence of words).
• Sequence input (e.g. sentiment analysis where a given sentence is classified as expressing a positive or negative sentiment).
• Sequence input and sequence output (e.g. Machine Translation: an RNN reads a sentence in English and then outputs a sentence in French).
• Synced sequence input and output (e.g. video classification where we wish to label each frame of the video)

License

This project is licensed under the MIT License - see the LICENSE.md file for details

by : Shahab Rahnama