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- W2585752437 abstract "Modelling requires comparison of model outputs to measurements, for calibration and verification. A key aspect data smoothing is to “filter out” noise. Often, data must be adjusted to a model’s time step (e.g. hourly to daily). For noisy data, LOWESS/LOESS (Locally Weighted Scatterplot Smoothing) is a popular piecewise regression technique. It produces a “smoothed” time series. LOWESS/LOESS is often used to visually assess the relationship between variables. The selection of LOWESS tuning parameters is usually performed on a visual trial and error basis. We investigate the so-called robust AIC (Akaike Information Criteria) for automatic selection of smoothing. Robust Pearson correlation coefficient and mean-squared error are employed to determine the polynomial degree of piecewise regression. The exclusion of outliers is attempted using a Hampel outlier identifier. We illustrate, assuming noisy linear data, how our proposed methods work for auto-tuning both the smoothing parameter and the degree of polynomial for LOWESS/ LOESS." @default.
- W2585752437 created "2017-02-10" @default.
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- W2585752437 date "2015-01-01" @default.
- W2585752437 modified "2023-09-26" @default.
- W2585752437 title "Automating the Smoothing of Time Series Data" @default.
- W2585752437 doi "https://doi.org/10.4172/2161-0525.1000304" @default.
- W2585752437 hasPublicationYear "2015" @default.
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