발간년도 : [2023]
| 논문정보 |
|
| 논문명(한글) |
[Vol.18, No.1] Stock Index Probabilistic Distribution Forecasting Using a Diffusion Model to Reflect the Volatility of Long-Term Sequences |
|
| 논문투고자 |
Young-jin Kim, Ha-young Kim |
|
| 논문내용 |
Many of the existing time series forecasting studies in stock market were statistical or machine learning methods based on point prediction. However, typical point forecasting methods fail to consider the distribution of data, which may ignore extreme market situations. This can be vulnerable especially for the long-term forecasting, given the high volatility in the stock market. To resolve the issue, we propose a stock index probabilistic distribution prediction framework based on the Denoising Diffusion Model, called StockGrad. Specifically, StockGrad adopt a sparse transformer encoder as feature extractor to yield zero probability for time steps that are less relevant to the prediction time step. For probability distribution prediction, our model transforms the white noise into the distribution of interest through the Markov chain using the TimeGrad time series generation methodology. Our proposed methodology can help investors make decisions on investment or risk management. Our experiments show that proposed StockGrad framework outperforms the existing deep learning probability models by about 3.71%, 1.53% and 1.71% on S&P500, NIKKEI225, and KOSPI200 stock index data sets, respectively. Also, it is experimentally demonstrated that using the sparse transformer encoder as a feature extractor captures historical time points related to predictive bulbs well, improve the performance of the long-term prediction. |
|
| 첨부논문 |
|
|
|
|
|
자료실 > 논문지 
