AI News, Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Prediction

Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Prediction

The goal of this project of mine is to bring users to try and experiment with the seq2seq neural network architecture.

Normally, seq2seq architectures may be used for other more sophisticated purposes than for signal prediction, let's say, language modeling, but this project is an interesting tutorial in order to then get to more complicated stuff.

You can find the French, original, version of this project in the French Git branch: Except the fact I made available an '.py' Python version of this tutorial within the repository, it is more convenient to run the code inside the notebook.

It is then that the notebook application (IDE) will open in your browser as a local server and it will be possible to open the .ipynb notebook file and to run code cells with CTRL+ENTER and SHIFT+ENTER, it is also possible to restart the kernel and run all cells at once with the menus.

Most of the time, you will have to edit the neural networks' training parameter to succeed in doing the exercise, but at a certain point, changes in the architecture itself will be asked and required.

A simple example would be to receive as an argument the past values of multiple stock market symbols in order to predict the future values of all those symbols with the neural network, which values are evolving together in time.

Note that it would be possible to obtain better results with a smaller neural network, provided better training hyperparameters and a longer training, adding dropout, and on.

Here is a prediction made on the actual future values, the neural network has not been trained on the future values shown here and this is a legitimate prediction, given a well-enough model trained on the task:

Disclaimer: this prediction of the future values was really good and you should not expect predictions to be always that good using as few data as actually (side note: the other prediction charts in this project are all 'average' except this one).

Other more creative input data could be sine waves (or other-type-shaped waves such as saw waves or triangles or two signals for cos and sin) representing the fluctuation of minutes, hours, days, weeks, months, years, moon cycles, and on.

It is also interesting to know where is the bitcoin most used: With all the above-mentionned examples, it would be possible to have all of this as input features, at every time steps: (BTC/USD, BTC/EUR, Dow_Jones, SP_500, hours, days, weeks, months, years, moons, meteo_USA, meteo_EUROPE, Twitter_sentiment).

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