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Development of a Model to Predict the Volatility of Housing Prices Using Artificial Intelligence
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- 발행기관
- 국제인공지능학회(구 한국인터넷방송통신학회) 바로가기
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- 간행물
- The International Journal of Advanced Smart Convergence 바로가기
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- 통권
- Volume 12 Number 4 (2023.12)바로가기
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- 페이지
- pp.75-87
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A440415
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- We designed to employ an Artificial Intelligence learning model to predict real estate prices and determine the reasons behind their changes, with the goal of using the results as a guide for policy. Numerous studies have already been conducted in an effort to develop a real estate price prediction model. The price prediction power of conventional time series analysis techniques (such as the widely-used ARIMA and VAR models for univariate time series analysis) and the more recently-discussed LSTM techniques is compared and analyzed in this study in order to forecast real estate prices. There is currently a period of rising volatility in the real estate market as a result of both internal and external factors. Predicting the movement of real estate values during times of heightened volatility is more challenging than it is during times of persistent general trends. According to the real estate market cycle, this study focuses on the three times of extreme volatility. It was established that the LSTM, VAR, and ARIMA models have strong predictive capacity by successfully forecasting the trading price index during a period of unusually high volatility. We explores potential synergies between the hybrid artificial intelligence learning model and the conventional statistical prediction model.
목차
Abstract
1. Introduction
2. Related works
2.1. ARIMA model
2.2. VAR model
2.3. LSTM model
3. Model to predict the volatility of housing prices
3.1. ARIMA data
3.2. VAR data
3.3. LSTM data
3.4. Configuring scenario and datasets
4. Experiment and its results
4.1. Performance comparison
4.2. Results of ARIMA
4.3. Results of VAR
4.4. Results of LSTM
5. Conclusions
Acknowledgement
References
키워드
Artificial Intelligent Bigdata Housing Price Prediction Volatility.저자
간행물 정보
발행기관
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- 발행기관명
- 국제인공지능학회(구 한국인터넷방송통신학회) [The International Association for Artificial Intelligence]
- 설립연도
- 2000
- 분야
- 공학>전자/정보통신공학
- 소개
- 인터넷방송, 인터넷 TV , 방송 통신 네트워크 및 관련 분야에 대한 국내는 물론 국제적인 학술, 기술의 진흥발전에 공헌하고 지식 정보화 사회에 기여하고자 한다.
간행물
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- 간행물명
- The International Journal of Advanced Smart Convergence
- 간기
- 계간
- pISSN
- 2288-2847
- eISSN
- 2288-2855
- 수록기간
- 2012~2025
- 십진분류
- KDC 326 DDC 380
이 권호 내 다른 논문 / The International Journal of Advanced Smart Convergence Volume 12 Number 4
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