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A comparative study between algorithms for time series forecasting on customer prediction: An investigation into the performance of ARIMA, RNN, LSTM, TCN and HMM
University of Skövde, School of Informatics.
2019 (English)Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Time series prediction is one of the main areas of statistics and machine learning. In 2018 the two new algorithms higher order hidden Markov model and temporal convolutional network were proposed and emerged as challengers to the more traditional recurrent neural network and long-short term memory network as well as the autoregressive integrated moving average (ARIMA).

In this study most major algorithms together with recent innovations for time series forecasting is trained and evaluated on two datasets from the theme park industry with the aim of predicting future number of visitors. To develop models, Python libraries Keras and Statsmodels were used.

Results from this thesis show that the neural network models are slightly better than ARIMA and the hidden Markov model, and that the temporal convolutional network do not perform significantly better than the recurrent or long-short term memory networks although having the lowest prediction error on one of the datasets. Interestingly, the Markov model performed worse than all neural network models even when using no independent variables.

Place, publisher, year, edition, pages
2019. , p. 52
Keywords [en]
machine learning, deep learning, time series forecasting, time series regression, data science, prediction, crisp-dm, keras, markov model, neural network, exploratory data analysis
Keywords [sv]
maskininlärning, djupinlärning, tidsserieprediktion, tidsserieprognos, neurala nätverk, markovmodell, explorativ dataanalys, dataanalys
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:his:diva-16974OAI: oai:DiVA.org:his-16974DiVA, id: diva2:1321224
External cooperation
Skara Sommarland AB, AB Furuviksparken
Subject / course
Informationsteknologi
Educational program
Information Systems - Business Intelligence
Supervisors
Examiners
Available from: 2019-06-10 Created: 2019-06-07 Last updated: 2019-06-10Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf