Time series analysis and prediction: Service request data from real estate

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master_Shehzad_Haseeb_2020.pdf (2.65 MB)

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Perustieteiden korkeakoulu | Master's thesis
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Date

2020-05-19

Department

Major/Subject

Machine Learning, Data Science and Artificial Intelligence (Macadamia)

Mcode

SCI3044

Degree programme

Master’s Programme in Computer, Communication and Information Sciences

Language

en

Pages

46+2

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Abstract

Time series data is generated by various domains, fields and industries and has found many applications with the increase in the availability of different type of data. In this thesis work, we analyzed the time series data of service requests from real estates and captured the yearly seasonality and trend from the past data and forecast the number of service requests for incoming months. We present two approaches to achieve our goal, structural time series and ARIMA models. We concluded that the structural time series performed better for predicting the future values of the data, although the seasonality and trend components from the past data were almost the same from both models.

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Supervisor

Marttinen, Pekka

Thesis advisor

Talvitie, Ossi

Keywords

time series, analysis, prediction, service requests, real estate

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https://urn.fi/URN:NBN:fi:aalto-202006213890

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[dipl] Perustieteiden korkeakoulu / SCI