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.
