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Supervised Temporal Autoencoder for Stock Return Time-series Forecasting

Financial markets are noisy learning environments. We propose an approach that regularizes the Temporal Convolutional Network using a supervised autoencoder, which we term the Supervised Temporal Autoencoder (STAE). We show that the addition of the auxiliary reconstruction task is beneficial to the primary supervised learning task in the context of stock return time-series forecasting. We also show that STAE is able to learn features directly from transformed price series, alleviating the need for handcrafted features. The autoencoder also improves interpretability as users can observe output of the decoder and inspect features retained by the network.