Time series prediction using 1-D Convolutional Neural Network for big data

The model is described in the associated Medium post: https://medium.com/p/168b47e54d54

Problem

The problem is divided into:

• Generate 2 datasets: one that will serve to train the model (100K time series), the other as unseen data (2.5M time series) on which to serve the model.

• Train a 1-D CNN model on train dataset

• Serve the model on unseen dataset (when serving mode) or on test set (when evaluation mode)

Files

• notebooks/data.json: configuration file for the data generation (temporarily included in the training and scoring codes)

• notebooks/config.json: model configuration file (temporarily included in the training and scoring codes)

• notebooks/synthetic_data_generation.py : the code for synthetic data generation

• notebooks/training.py: the code for model training

• notebooks/scoring.py: the code for model serving / evaluation

• notebooks/utils.py: the python module containing helper functions

old-autoencoder folder (old): This folder contains examples of how to perform time series forecast using LSTM autoencoders and 1-d convolutional neural networks in Keras