Arturus/kaggle-web-traffic resource

Health v2 · maintenance only

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1849 stars · 657 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

The 1st place solution code for the Kaggle Web Traffic Time Series Forecasting competition, implementing an RNN encoder-decoder (seq2seq) model in TensorFlow. It includes feature building, training, and prediction pipelines, and serves as a reference for time-series forecasting with deep learning.

Use cases

• forecast web traffic time series
• learn how to build a seq2seq time-series model
• reproduce a Kaggle competition winning solution
• study RNN encoder-decoder forecasting in TensorFlow
• apply deep learning to multi-series forecasting

When to choose

• you want a proven reference architecture for time-series forecasting with RNNs
• you are competing in or studying the Kaggle web traffic forecasting problem
• you have GPU resources and want to reproduce the competition results

When to avoid

• you need a production-ready forecasting library or maintained tool
• you lack a GPU, since training requires cuDNN
• you want a general-purpose time-series package rather than competition-specific code

Facets

learning-resource · maturity maintenance

machine-learning deep-learning llm-training time-series machine-learning data-science python kaggle-competition seq2seq rnn-encoder-decoder tensorflow time-series-forecasting cudnn winning-solution gpu linux

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