| Title |
Investigating Problem Analysis and Solution Strategies in Predicting Housing Price IndexThrough Sequential Application of Transformer and LSTM Models |
| Authors |
윤도경(Yoon, Do-Kyung) ; 신동윤(Shin, Dong-Yoon) |
| DOI |
https://doi.org/10.5659/JAIK.2024.40.1.35 |
| Keywords |
LSTM; Transformer; Housing Price Prediction; Problem Analysis; Solution Strategies; Machine Learning; Predictive Modeling; Error Analysis |
| Abstract |
This study aims to review previous research on factors affecting the housing price index and construct a prediction model for the index using
Long Short-Term Memory (LSTM) and Transformer models. Specifically, it combines LSTM, specialized in processing time-series data, and
DistilBERT, specialized in handling text data, to utilize both historical housing price index data and relevant news articles. The experimental
results of the proposed model confirmed significant accuracy when comparing predicted values in each region (J, S, G) with the actual
values. However, some clusters displayed relatively high errors, indicating a need for additional analysis and improvement. Additionally, it
was observed that subjective elements could significantly impact the interpretation of clustering results, highlighting the necessity for further
analysis. Result visualization and statistical analysis were conducted, confirming their accurate reflection of housing price fluctuation trends in
each region. This study introduces a novel approach to predicting the housing price index using deep learning models like LSTM and
DistilBERT, providing valuable insights into real estate market trend predictions. The approaches and findings from this research are
anticipated to provide valuable starting points for further exploration of creative solutions and the development of effective problem-solving
strategies. |