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- W1987968712 abstract "Developing a precise understanding of the dynamic behavior of time series is crucial for the success of forecasting techniques. We introduce a novel communication-theoretic framework for modeling and forecasting time series. In particular, the observed time series is modeled as the output of a noisy communication system with the input as the future values of time series. We use a data-driven probabilistic approach to estimate the unknown parameters of the system which in turn is used for forecasting. We also develop an extension of the proposed framework together with a filtering algorithm to account for the noise and heterogeneity in the quality of time series. Experimental results demonstrate the effectiveness of this approach." @default.
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- W1987968712 date "2015-09-01" @default.
- W1987968712 modified "2023-09-26" @default.
- W1987968712 title "Time series forecasting via noisy channel reversal" @default.
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- W1987968712 doi "https://doi.org/10.1109/mlsp.2015.7324330" @default.
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