TY - JOUR
T1 - A seasonal-trend decomposition-based dendritic neuron model for financial time series prediction
AU - He, Houtian
AU - Gao, Shangce
AU - Jin, Ting
AU - Sato, Syuhei
AU - Zhang, Xingyi
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/9
Y1 - 2021/9
N2 - Financial time series prediction is a hot topic in machine learning field, but existing works barely catch the point of such data. In this study, we employ the most suitable preprocessing technology, machine learning model, and training algorithm to construct a novel seasonal-trend decomposition-based dendritic neuron model (STLDNM) to tackle this issue. The model's unique part is to use the seasonal-trend decomposition based on loess (STL) as preprocessing technology. Particularly, the STL can extract seasonal and trend features from the original data, so that a simple polynomial fitting method can be used to handle these sub-series. Next, the remained complex residual component is predicted by an anti-overfitting dendritic neuron model (DNM) trained by an efficient back-propagation algorithm. Finally, the processed components are added up to obtain the predicting result. sixteen real-world stock market indices are used to test STLDNM. The experimental results show that it can perform significantly better than other previous convinced models under different assessment criteria. This model successfully reveals the internal feature of financial data and certainly improves the predicting accuracy due to the rightful methodology selection. Therefore, the newly designed STLDNM not only has high potentials for practical applications in the financial aspect but also provides novel inspirations for complex time series prediction problem researchers.
AB - Financial time series prediction is a hot topic in machine learning field, but existing works barely catch the point of such data. In this study, we employ the most suitable preprocessing technology, machine learning model, and training algorithm to construct a novel seasonal-trend decomposition-based dendritic neuron model (STLDNM) to tackle this issue. The model's unique part is to use the seasonal-trend decomposition based on loess (STL) as preprocessing technology. Particularly, the STL can extract seasonal and trend features from the original data, so that a simple polynomial fitting method can be used to handle these sub-series. Next, the remained complex residual component is predicted by an anti-overfitting dendritic neuron model (DNM) trained by an efficient back-propagation algorithm. Finally, the processed components are added up to obtain the predicting result. sixteen real-world stock market indices are used to test STLDNM. The experimental results show that it can perform significantly better than other previous convinced models under different assessment criteria. This model successfully reveals the internal feature of financial data and certainly improves the predicting accuracy due to the rightful methodology selection. Therefore, the newly designed STLDNM not only has high potentials for practical applications in the financial aspect but also provides novel inspirations for complex time series prediction problem researchers.
KW - Artificial neural network
KW - Dendritic neuron model
KW - Financial time series prediction
KW - Machine learning
KW - Preprocessing technology
KW - Seasonal-trend decomposition
KW - Separate processing
UR - http://www.scopus.com/inward/record.url?scp=85105518624&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2021.107488
DO - 10.1016/j.asoc.2021.107488
M3 - 学術論文
AN - SCOPUS:85105518624
SN - 1568-4946
VL - 108
JO - Applied Soft Computing
JF - Applied Soft Computing
M1 - 107488
ER -