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...
| Main Authors: | , |
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| Format: | Others |
| Language: | English |
| Published: |
Uppsala universitet, Statistiska institutionen
2019
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| Subjects: | |
| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385823 |