  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.
