WebTraffic

Web sessions information from an events company based in South Africa.

The goal is to predict the number of web sessions in 4 different regions in South Africa.

The data consists of 15 minute interval traffic values between '2023-06-16 00:00:00' and '2023-09-15 23:45:00' for each region. Two types of sessions are captured sessionsA and sessionsB. The isMissing flag is equal to 1 if any of the servers failed to capture sessions, otherwise if all servers functioned properly this flag is equal to 0.

Things to consider:

• region R5 captures sessions in backup mode. Strictly speaking, R5 is not necessary to predict. * Can sessionsA and sessionsB events be predicted accurately for each region over the next day (next 96 intervals)? * What is the best way to deal with the missing values? * How can model selection be used (a multi-model approach)? * Can dependence (correlation) between regions be utilised for more accurate predictions? * Can both sessionA and sessionB be predicted simultaneously with one model?

This dataset is well suited for time series forecasting models, as well as anomaly detection methods. Ideally, the goal is to build a time series forecasting model that is robust to the anomalous events and generalise well on normal operating conditions.

Attributes

• desc

  Return the description from the docstring.

• is_downloaded

  Indicate whether or the data has been correctly downloaded.

• path

Methods

download

take

Iterate over the k samples.

Parameters

• k — 'int'