Web Traffic Forecasting presents a solution for predicting daily Wikipedia article views. This project uses a modified WaveNet architecture, a deep neural network, to forecast traffic patterns. The core problem is accurate time-series prediction with a focus on handling long forecasting horizons and potential error accumulation.
The project employs a WaveNet-like architecture adapted for long-term forecasting, addressing the challenge of error propagation in sequence generation. A sequence-to-sequence approach with separate encoder and decoder parameters is utilized to improve prediction coherence. The model was trained to minimize loss when unravelled for the entire forecast horizon.
- Model: TensorFlow-based dilated causal convolutions for time series modeling.
- Data Handling: Processes a large time series dataset of daily Wikipedia article views.
- Evaluation: Uses Symmetric Mean Absolute Percentage Error (SMAPE) for performance assessment.
- Dependencies: Requires Python 2.7 and TensorFlow 1.3.0.
- GPU Support: Recommended 12 GB GPU for efficient training.
- Sequence Length: Designed to handle forecasts up to 64 days into the future.
- Adaptability: Modifies WaveNet to generate coherent predictions over extended time horizons.
The project is a completed solution to a Kaggle competition, indicating a functional and relatively stable implementation. The last commit occurred in 2018, suggesting the project has been maintained to some extent. While documentation is present in the README, completeness may vary. The project has garnered a sizable number of stars and forks, indicating community interest.
This project provides a working solution for web traffic forecasting, particularly useful for analyzing and predicting website traffic patterns. Data scientists and machine learning engineers can leverage this code to build or improve time series forecasting models, especially with long-range predictions. It demonstrates practical application of deep learning techniques to real-world time series problems.