Innovation (signal processing)
difference of forecasted and actual values
In time series analysis (or forecasting) — as conducted in statistics, signal processing, and many other fields — the innovation is the difference between the observed value of a variable at time t and the optimal forecast of that value based on information available prior to time t. If the forecasting method is working correctly, successive innovations are uncorrelated with each other, i.e., constitute a white noise time series. Thus it can be said that the innovation time series is obtained from the measurement time series by a process of 'whitening', or removing the predictable component. The use of the term innovation in the sense described here is due to Hendrik Bode and Claude Shannon (1950) in their discussion of the Wiener filter problem, although the notion was already implicit in the work of Kolmogorov.
In contrast, the residual is the difference between the observed value of a variable at time t and the optimal updated state of that value based on information available till (including) time t.
“Innovation (signal processing)” enters the record as difference of forecasted and actual values. Crown Archives preserves that source wording while asking what Innovation, signal and processing can confirm, complicate or overturn.
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Vocabulary and entity names are the principal evidence signals here, because they determine the precision of every later search. The source revision retrieved here is dated Sep 20, 2026. The linked authority identifier is Q6036164. The first chronological checks are 1950.
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This entry incorporates text from “Innovation (signal processing)” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.