  The application of deep learning to time series forecasting is one of the
major challenges in present machine learning. We propose a novel methodology
that combines machine learning and image processing methods to define and
predict market states with intraday financial data. A wavelet transform is
applied to the log-return of stock prices for both image extraction and
denoising. A convolutional neural network then extracts patterns from denoised
wavelet images to classify daily time series, i.e. a market state is associated
with the binary prediction of the daily close price movement based on the
wavelet image constructed from the price changes in the first hours of the day.
This method overcomes the low signal-to-noise ratio problem in financial time
series and gets a competitive prediction accuracy of the market states 'Up' and
'Down' of financial data as tested on the S&P 500.
