Downsampling with CUSUM Filter
Filtering out the noise and keeping only the informative parts of your data.
Typically financial time series suffer from a low signal-to-noise ratio. When the entire financial dataset is used the model will focus too much on noisy samples and not enough on highly informative samples. A way to improve the signal-to-noise ratio is to downsample the dataset, but randomly downsampling is not effective as the ratio of noisy to informative sample will persist. Instead one could apply a CUSUM filter which only creates a sample when the next values deviate sufficiently from the previous value.
Consider a locally stationary process generating IID observations
. The cumulative sums can then be defined as
with boundary condition
A sample is only created when
for some threshold
This can be further extended to a symmetric CUSUM filter to include run-ups and run-downs such that