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Time series prediction of web traffic data

Dominguez Berndtsson, Nils LU (2021) STAN40 20201
Department of Statistics
Abstract
In this thesis we predict web traffic intensity levels for 7 customers of a cybersecurity company.
The models we predict with are a SARIMA model and a Temporal convolutional network.
The quality of the predictions vary a lot between the different customers.
The predictions improve when performed on data that is logged, demeaned and differenced.
Please use this url to cite or link to this publication:
author
Dominguez Berndtsson, Nils LU
supervisor
organization
course
STAN40 20201
year
type
H1 - Master's Degree (One Year)
subject
keywords
Time series predictions, Temporal Convolutional Network, SARIMA, Web traffic, Cybersecurity
language
English
id
9057479
date added to LUP
2021-12-14 11:12:33
date last changed
2021-12-14 11:12:33
@misc{9057479,
  abstract     = {{In this thesis we predict web traffic intensity levels for 7 customers of a cybersecurity company.
The models we predict with are a SARIMA model and a Temporal convolutional network.
The quality of the predictions vary a lot between the different customers.
The predictions improve when performed on data that is logged, demeaned and differenced.}},
  author       = {{Dominguez Berndtsson, Nils}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Time series prediction of web traffic data}},
  year         = {{2021}},
}