Calculating the median of data points within a moving window is a common task in fields like finance, real-time analytics and signal processing. The main applications are anomal- and outlier-detection / removal.
A naive implementation based on sorting is costly—especially for large window sizes. An elegant solution is the Two-Heaps Rolling Median algorithm, which maintains two balanced collections to quickly calculate the median as new data arrives and old data departs.
Continue readingIn finance and signal processing, detecting trends or smoothing noisy data streams efficiently is crucial. A popular tool for this task is a linear regression applied to a sliding (rolling) window of data points. This approach can serve as a low-pass filter or a trend detector, removing short-term fluctuations while preserving longer-term trends. However, naive methods for sliding-window regression can be computationally expensive, especially as the window grows larger, since their complexity typically scales with window size.
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