Original answer:
Short answer: Use a convolutional autoencoder (add convolution layers to the exterior of the autoencoder)
Long answer: From my experience with time series and autoencoders, it is best to do as much feature extraction as possible outside the autoencoder as it's more difficult to train them to to do the feature extraction and dimensionality reduction. Consider using FFT or wavelet transforms on your data first. Even if they don't extract your pattern exactly, it helps many applications. After transforming the data, train the convolutional autoencoder using the features and then to evaluate your model, reverse the transformation and compare with the original.