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

• 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

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.