Time Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cells

Time Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cells

In this thesis, we examine the performance of different forecasting methods. We use dataof monthly house prices from the larger Stockholm area and the municipality of Uppsalabetween 2005 and early 2019 as the time series to be forecast. Firstly, we compare theperformance of two machine learning meth...

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Bibliographic Details
Main Authors: Rostami, Jako, Hansson, Fredrik
Format: Others
Language:English
Published: Uppsala universitet, Statistiska institutionen 2019
Subjects:
machine learning
cross-validation
seasonality
sliding window
sequential model
supervised learning
Probability Theory and Statistics
Sannolikhetsteori och statistik
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385823

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385823

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