Recurrent Neural Network for Time Series Forecasting

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This is a time series forecasting project based on the Wikipedia Web Traffic Time Series Forecasting dataset from Kaggle. Two RNN architectures are implemented:

• A "Vanilla" RNN regressor.
• A Seq2seq regressor.

Both are implemented in TensorFlow 2, with custom training functions optimized with Autograph.

Structure of the repository

Main files:

• config.yaml: config file for hyperparameters.
• dataprep.py: data preprocessing pipeline.
• train.py: training pipeline.
• tools.py: contains useful processing functions to be iterated in main pipelines.
• model.py: builds model.

I also added a visualize_performance.ipynb Jupyter Notebook to visually inspect models' performance on Test data.

Folders:

• /data_raw/: requires unzipped train_2.csv file from Kaggle. Available is an imputed.csv dataset, containing imputed time series, coming from my other repository on a GAN for imputation of missing data in time series.
• /data_processed/: divided in /Train/ and /Test/ directories.
• /saved_models/: contains all saved TensorFlow models, both regressors.
• /utils/: for pics and other secondary files.

How to run code

After you clone the repository locally, download the raw dataset from Kaggle, and place unzipped train_2.csv file in /data_raw/ folder. Then, time series forecast is executed in two steps. First, run data preprocessing pipeline:

python -m dataprep

This will generate Training+Validation and Test files, stored in /data_processed/ subdirectories. Second, launch training pipeline with:

python -m train

This will either create, train and save a new model, or load and train an already existing one, stored in /saved_models/ folder.

Finally, Test set performance will be evaluated from test.ipynb notebook.

Modules

numpy==1.18.3
pandas==1.0.3
scikit-learn==0.22.2.post1
scipy==1.4.1
tensorflow==2.1.0
tqdm==4.45.0

Hardware

I used a pretty powerful laptop, with 64GB or RAM and NVidia RTX 2070 GPU. I highly recommend GPU training to avoid excessive computational times.