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Forecasting future traffic to Wikipedia pages using AR MA ARIMA : Removing trend and seasonality with decomposition

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Kaggle-Web-Traffic-Time-Series-Forecasting

Forecasting future traffic to Wikipedia pages using AR MA ARIMA : Removing trend and seasonality with decomposition

This competition focuses on the problem of forecasting the future values of multiple time series, as it has always been one of the most challenging problems in the field. More specifically, we aim the competition at testing state-of-the-art methods designed by the participants, on the problem of forecasting future web traffic for approximately 145,000 Wikipedia articles.

My aim is to make our resolute efforts count by working on different time series forecasting model like AR, MA, ARIMA as it has always been one of the most challenging task to do. In that manner we compared the accuracy of different time series models and also build up a model that forecast WIKIPEDIA TRAFFIC using approximately 1,45,000 Wikipedia articleswith their total views from 1 July, 2015 to 31 Dec, 2016.

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Forecasting future traffic to Wikipedia pages using AR MA ARIMA : Removing trend and seasonality with decomposition

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