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- W3016398929 abstract "In this paper, we present a novel and effective technique for short-term traffic flow forecasting. Our main contribution is an extension of Kalman filter, such that it becomes to be able to identify the noise and then filter out it; we hence named the present technique as noise-identified Kalman filter. Our epistemological perspective is that the classic Kalman filter filters out not only the noise but also useful signals. We hence develop the Kalman filter for de-noising while preserving the useful signals by devising a cost function. By conducting extensive experiments on four benchmark data sets, the proposed technique is firmly verified to be effective for short-term traffic flow forecasting, outperforming not only the classic Kalman filter but also other frequently-used parametric and non-parametric techniques." @default.
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- W3016398929 date "2019-12-01" @default.
- W3016398929 modified "2023-09-24" @default.
- W3016398929 title "Noise-Identified Kalman Filter for Short-Term Traffic Flow Forecasting" @default.
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- W3016398929 doi "https://doi.org/10.1109/msn48538.2019.00093" @default.
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