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
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spelling ndltd-UPSALLA1-oai-DiVA.org-uu-3858232019-06-25T09:10:20ZTime Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cellsengRostami, JakoHansson, FredrikUppsala universitet, Statistiska institutionenUppsala universitet, Statistiska institutionen2019machine learningcross-validationseasonalitysliding windowsequential modelsupervised learningProbability Theory and StatisticsSannolikhetsteori och statistikIn 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 methods, the Long Short-Term Memory, and theSupport Vector Machine methods. The two methods forecasts are compared, and themodel with the lowest forecasting error measured by three metrics is chosen to be comparedwith a classic seasonal ARIMA model. We find that the Long Short-Term Memorymethod is the better performing machine learning method for a twelve-month forecast,but that it still does not forecast as well as the ARIMA model for the same forecast period. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385823application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic machine learning
cross-validation
seasonality
sliding window
sequential model
supervised learning
Probability Theory and Statistics
Sannolikhetsteori och statistik
spellingShingle machine learning
cross-validation
seasonality
sliding window
sequential model
supervised learning
Probability Theory and Statistics
Sannolikhetsteori och statistik
Rostami, Jako
Hansson, Fredrik
Time Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cells
description 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 methods, the Long Short-Term Memory, and theSupport Vector Machine methods. The two methods forecasts are compared, and themodel with the lowest forecasting error measured by three metrics is chosen to be comparedwith a classic seasonal ARIMA model. We find that the Long Short-Term Memorymethod is the better performing machine learning method for a twelve-month forecast,but that it still does not forecast as well as the ARIMA model for the same forecast period.
author Rostami, Jako
Hansson, Fredrik
author_facet Rostami, Jako
Hansson, Fredrik
author_sort Rostami, Jako
title Time Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cells
title_short Time Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cells
title_full Time Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cells
title_fullStr Time Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cells
title_full_unstemmed Time Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cells
title_sort time series forecasting of house prices: an evaluation of a support vector machine and a recurrent neural network with lstm cells
publisher Uppsala universitet, Statistiska institutionen
publishDate 2019
url http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385823
work_keys_str_mv AT rostamijako timeseriesforecastingofhousepricesanevaluationofasupportvectormachineandarecurrentneuralnetworkwithlstmcells
AT hanssonfredrik timeseriesforecastingofhousepricesanevaluationofasupportvectormachineandarecurrentneuralnetworkwithlstmcells
_version_ 1719209259657330688

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