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Time Series Analysis with a Generaized Additive Model

To study page view trends for a Wikipedia article on Daylight Saving Time, we first used a Python script to extract the data from a Wikipedia database. Next, we used a GAM package called Prophet published by Facebook researchers to conduct our time series analysis in Python. The package is also available in R.

Accompanying tutorial here: https://algobeans.com/2017/04/04/laymans-tutorial-time-series-analysis/

Overall, annual, weekly and holiday trends:

Component trends predicting page views of DST Wikipedia article.

Validating the model with simulated historical forecasts:

Simulated historical forecasts of DST's Wikipedia page views.

Examining error rates:

Prediction errors across the forecast horizon. The red line represents the mean absolute error across the 11 simulated forecasts, while the black line is a smoothed trend of that error.

Comparing error rates across models to check effects of parameters:

Comparison of prediction errors resulting from different prior values.

References:

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Code to 1) scrap wikipedia page view counts, and to 2) conduct time series analysis with GAM

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