Time Series Prediction using Adaptive Filtering

Author(s): Satsawat Natakarnkitkul

Originally published on Towards AI.

Simple implementation example

Adaptive filtering is a computational device that attempts to model the relationship between two signals, whose coefficients change with an objective to make the filter converge to an optimal state. The optimization criterion is a cost function, which is most commonly the mean square of the error between the output of the adaptive filter and the desired signal. The mean square error (MSE) will converge to its minimal value, while the filter adapts its coefficients. The figure below demonstrates the simple adaptive filter.

Simple adaptive filter toolbox

The adaptive filter will try to match the filter output, y(k), with the desired signal,… Read the full blog for free on Medium.

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Published via Towards AI