Decomposition of Multiple Time-Scale Systems

2021 ◽  
Author(s):  
Dmitry Makarov ◽  
Vladimir Sobolev
Keyword(s):  
Time Scale ◽  
Multiple Time ◽  
Multiple Time Scale

Decentralized monthly generation scheduling of cascade hydropower plants in multiple time scale markets

2022 ◽  
Vol 135 ◽  
pp. 107420
Author(s):  
Yuhang Ma ◽  
Yuan Huang ◽  
Gang Wu ◽  
Junyong Liu ◽  
Yang Liu ◽  
...  
Keyword(s):  
Time Scale ◽  
Multiple Time ◽  
Multiple Time Scale ◽  
Generation Scheduling ◽  
Hydropower Plants

The plasma transport equations derived by multiple time‐scale expansions. II. An application

1995 ◽  
Vol 2 (4) ◽  
pp. 1206-1216 ◽  
Author(s):  
J. W. Edenstrasser ◽  
M. M. M. Kassab
Keyword(s):  
Time Scale ◽  
Transport Equations ◽  
Plasma Transport ◽  
Multiple Time ◽  
Multiple Time Scale

Predicting the trend of stock index based on feature engineering and CatBoost model

2021 ◽  
pp. 2150027
Author(s):  
Renzhe Xu ◽  
Yudong Chen ◽  
Tenglong Xiao ◽  
Jingli Wang ◽  
Xiong Wang
Keyword(s):  
Time Scale ◽  
Stock Index ◽  
Feature Engineering ◽  
Multiple Time ◽  
Multiple Time Scale ◽  
Back Testing ◽  
Price Series ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
Sharp Ratio

As an important tool to measure the current situation of the whole stock market, the stock index has always been the focus of researchers, especially for its prediction. This paper uses trend types, which are received by clustering price series under multiple time scale, combined with the day-of-the-week effect to construct a categorical feature combination. Based on the historical data of six kinds of Chinese stock indexes, the CatBoost model is used for training and predicting. Experimental results show that the out-of-sample prediction accuracy is 0.55, and the long–short trading strategy can obtain average annualized return of 34.43%, which is a great improvement compared with other classical classification algorithms. Under the rolling back-testing, the model can always obtain stable returns in each period of time from 2012 to 2020. Among them, the SSESC’s long–short strategy has the best performance with an annualized return of 40.85% and a sharp ratio of 1.53. Therefore, the trend information on multiple time-scale features based on feature engineering can be learned by the CatBoost model well, which has a guiding effect on predicting stock index trends.

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