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Market financial forecasting is a trending area in deep learning. Deep
learning models are capable of tackling the classic challenges in stock market
data, such as its extremely complicated dynamics as well as long-term temporal
correlation. To capture the temporal relationship among these time series,
recurrent neural networks are employed. However, it is difficult for recurrent
models to learn to keep track of long-term information. Convolutional Neural
Networks have been utilized to better capture the dynamics and extract features
for both short- and long-term forecasting. However, semantic segmentation and
its well-designed fully convolutional networks have never been studied for
time-series dense classification. We present a novel approach to predict
long-term daily stock price change trends with fully 2D-convolutional
encoder-decoders. We generate input frames with daily prices for a time-frame
of T days. The aim is to predict future trends by pixel-wise classification of
the current price frame. We propose a hierarchical CNN structure to encode
multiple price frames to multiscale latent representation in parallel using
Atrous Spatial Pyramid Pooling blocks and take that temporal coarse feature
stacks into account in the decoding stages. Our hierarchical structure of CNNs
makes it capable of capturing both long and short-term temporal relationships
effectively. The effect of increasing the input time horizon via incrementing
parallel encoders has been studied with interesting and substantial changes in
the output segmentation masks. We achieve overall accuracy and AUC of %78.18
and 0.88 for joint trend prediction over the next 20 days, surpassing other
semantic segmentation approaches. We compared our proposed model with several
deep models specifically designed for technical analysis and found that for
different output horizons, our proposed models outperformed other models.