Predict time series
A TensorFlow-based educational project for learning seq2seq RNNs through signal forecasting exercises.
What is Predict time series?
seq2seq-signal-prediction is an educational TensorFlow project that teaches how to implement Sequence-to-Sequence Recurrent Neural Networks for time series forecasting. It provides four progressive exercises where users build models to predict signals, from simple deterministic patterns to real-world Bitcoin price data. The project demonstrates how encoder-decoder architectures can be used for multidimensional forecasting and signal denoising.
Machine learning practitioners and students who want hands-on experience with seq2seq RNN architectures for time series prediction. It's particularly valuable for those learning TensorFlow implementation of recurrent networks and seeking practical forecasting examples.
Developers choose this project for its structured, exercise-based approach to learning seq2seq RNNs, with clear examples ranging from basic signal prediction to real-world financial forecasting. It provides complete TensorFlow implementations with educational explanations, unlike many theoretical tutorials.
Overview
Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier
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