A convolutional auto-encoder for compressing time sequence data of stocks.
Similar to full-connection autoencoder(https://github.com/melissa135/Denoise_AutoEncoder), but more suitable for time series data.
AutoEncoder ( (encoder): Sequential ( (0): Conv1d(1, 5, kernel_size=(4,), stride=(4,)) (1): Tanh () (2): Conv1d(5, 10, kernel_size=(4,), stride=(4,)) (3): Tanh () (4): Conv1d(10, 5, kernel_size=(3,), stride=(3,)) (5): Tanh () ) (decoder): Sequential ( (0): ConvTranspose1d(5, 10, kernel_size=(3,), stride=(3,)) (1): Tanh () (2): ConvTranspose1d(10, 5, kernel_size=(4,), stride=(4,)) (3): Tanh () (4): ConvTranspose1d(5, 1, kernel_size=(4,), stride=(4,)) ) ) The loss sequence on trainset and testset, shows the less loss and smoother curve comparing to full-connection autoencoder. 
The original 5-minute K line sequnce and the recovered sequence from compressed vector. 