We consider the problem of causal estimation, i.e., filtering, of a real-valued signal corrupted by zero mean, time-independent, real-valued additive noise, under the mean-squared error (MSE) criterion. We build a universal filter whose per-symbol squared error, for every bounded underlying signal, is essentially as small as that of the best finite-duration impulse response (FIR) filter of a given order. We do not assume a stochastic mechanism generating the underlying signal, and assume only that the variance of the noise is known to the filter. The regret of the expected MSE of our scheme is shown to decay as O(log n/n), where n is the length of the signal. Moreover, we present a stronger concentration result which guarantees the performance of our scheme not only in expectation, but also with high probability. Our result implies a conventional stochastic setting result, i.e., when the underlying signal is a stationary process, our filter achieves the performance of the optimal FIR filter. We back our theoretical findings with several experiments showcasing the potential merits of our universal filter in practice. Our analysis combines tools from the problems of universal filtering and competitive on-line regression.
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