Wikipedia Traffic Forecasting

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Presentation transcript:

Wikipedia Traffic Forecasting Compiled by- Divya Lingwal Karishma Patil Shivani Mathur Monalisa Singh

Introduction It is important to predict the websites future traffic volume, in order to provide a quality of service to the users. We are implementing the process of Data analysis for Wikipedia Traffic using the time-series approach. Predicting the future behaviour of time series for Wikipedia articles.

Problem Statement Wikipedia has large number of potential users viewing approximately 145,000 Wikipedia articles. It is essential to deal with the problem of overload, for that we need to predict future web traffic for thousands of Wikipedia articles.

Project Description AIM: To help predict future views of Wikipedia articles Using Time series analysis which encapsulates problems like analysis, inference, classification and forecast. We are using R programming language. Used both Time series analysis and forecasting techniques. Analyzed all 4 components of Time series. Datasets used: User view data, each column is a date and each row is an article Mapping between article names and a unique ID column

Brief Introduction of Wikimedia and Mediawiki

Procedure Data Cleaning Data transformation Time Series extraction Parameter extraction Forecasted Methods

Data Cleaning Finding the missing values-8% missing values Few missing values. These seem to be important so we did not remove them

Data Transformation Split data into 2 parts: ‘Page’ and ‘Dates’

Data Transformation cont’d Divided the article info from *wikipedia*, *wikimedia*, and *mediawiki*

Data Transformation cont’d Tokenized the pages into different columns Access: Desktop, Mobile-web. All-access Agent: All-agents, Spider Locale: wikmed, medwik, codes(zh,en,ja,es,fr) for wikipedia

Experimental Evaluations Un-Smoothened Graph Need to smooth it to check for better patterns and trends

Smoothing

Normalized Time series

Scale changed to visualize better

Curve according to specifications

Data Overview Comparison of articles in 7 languages: German, Chinese, English, Spanish, French, Japanese, Russian. Higher frequency for english articles Slightly higher number of mobile viewers than Desktop viewers

Calculating Time Series parameters To analyze the time series data we need to find the following parameters: Mean Standard Deviation Amplitude Slope Linear model for slope: Views~ pages+dates

Individual observations with extreme parameters Finding out the values with highest views

Time-Series Visualization of Top 4 Articles

Plotting time series for shorter duration Plotting time series for 2-month duration

Forecasting Approach In our project we forecast 2 months data and measure the prediction accuracy by keeping a holdout sample of 60 days from our forecast data. We have done this by using autoregressive integrated moving average(ARIMA) model. This consists of three parts( parameterized by indices p, d, q ) as ARIMA(p, d, q): Auto-regressive/p: p indicates the range of lags. Integrated/d: d is a differencing parameter, which gives us the number of times we are subtracting the current and the previous values of a time series. Moving average/q: q gives us the number of previous error terms to include in the regression error of the model.

Forecasts

Future Work Seasonality component Holt- Winters method

THANK YOU