Multi-Scale RCNN Model for Financial Time-series Classification
This work addresses the challenge of accurate and profitable financial time-series classification for investment management, representing an incremental improvement by combining existing properties more effectively.
The paper tackled the problem of financial time-series classification by proposing a Multi-Scale Temporal Dependent Recurrent Convolutional Neural Network (MSTD-RCNN) to effectively combine multi-scale and temporal dependency properties, achieving state-of-the-art performance in trend classification and simulated trading on Chinese stock market datasets.
Financial time-series classification (FTC) is extremely valuable for investment management. In past decades, it draws a lot of attention from a wide extent of research areas, especially Artificial Intelligence (AI). Existing researches majorly focused on exploring the effects of the Multi-Scale (MS) property or the Temporal Dependency (TD) within financial time-series. Unfortunately, most previous researches fail to combine these two properties effectively and often fall short of accuracy and profitability. To effectively combine and utilize both properties of financial time-series, we propose a Multi-Scale Temporal Dependent Recurrent Convolutional Neural Network (MSTD-RCNN) for FTC. In the proposed method, the MS features are simultaneously extracted by convolutional units to precisely describe the state of the financial market. Moreover, the TD and complementary across different scales are captured through a Recurrent Neural Network. The proposed method is evaluated on three financial time-series datasets which source from the Chinese stock market. Extensive experimental results indicate that our model achieves the state-of-the-art performance in trend classification and simulated trading, compared with classical and advanced baseline models.