Abstract
We introduce Random Projection Filter Bank (RPFB) as a general framework for feature extraction from time series data. RPFB is a set of randomly generated stable autoregressive filters that are convolved with the input time series. Filters in RPFB extract different aspects of the time series, and together they provide a reasonably good summary of the time series. These features can then be used by any conventional machine learning algorithm for solving tasks such as time series prediction, and fault detection and prognosis with time series data. RPFB is easy to implement, fast tocompute, and parallelizable. Through a series of experiments we show that RPFB alongside conventional machine learning algorithms can be effective in solving data-driven fault detection and prognosis problems.
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