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House Price Prediction Approach based on Deep Learning and ARIMA Model
- Source :
- 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT).
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- The nonlinear relationship between influential factors and house price and inadequate number of sample size could be the cause of the poor performance of the traditional models. Meanwhile, the daily data of the real estate market is very huge and it is increasing rapidly. The traditional house price prediction approaches are lack of capacity for massive data analysis, causing low utilization of data. To address these concerns, a house price prediction model based on deep learning is proposed in this paper, implemented on the TensorFlow framework. Adam optimizer is used to train the model, where Relu function is adopted to be the activation function. Then house price trend is predicted based on the ARIMA model. By using Scrapy, housing data are obtained from Internet to be the experimental dataset. Comparative experiments were conducted between the proposed approach and SVR method. The experimental results show that individual house price predicted by the proposed approach is better than that of SVR method. And the predicted house price trend is mainly agreement with the real situation.
- Subjects :
- Computer science
business.industry
Deep learning
Activation function
Real estate
02 engineering and technology
Function (mathematics)
01 natural sciences
010104 statistics & probability
Nonlinear system
Sample size determination
0202 electrical engineering, electronic engineering, information engineering
Econometrics
020201 artificial intelligence & image processing
The Internet
Artificial intelligence
Autoregressive integrated moving average
0101 mathematics
business
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT)
- Accession number :
- edsair.doi...........1f81f772a7b076bf31d16e8f7701456e