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- W2912941569 abstract "Abstract Prior to formally analysing time series, it is often appropriate to transform the data. Three general classes of transformations are introduced: distributional, stationarity inducing, and decompositional. Distributional transformations, such as taking logarithms or using the more sophisticated power and inverse hyperbolic sine transformations, help to induce normality, which typically simplifies statistical analysis. Stationarity-inducing transformations are based on differencing the data and may involve taking proportional or percentage changes, so defining, for example, rates of inflation and returns. Decompositional transformations may include the smoothing of a time series by taking moving averages and splitting the series into various components, such as trend, cycle, seasonal, and irregular." @default.
- W2912941569 created "2019-02-21" @default.
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- W2912941569 date "2019-01-01" @default.
- W2912941569 modified "2023-09-24" @default.
- W2912941569 title "Transforming Time Series" @default.
- W2912941569 doi "https://doi.org/10.1016/b978-0-12-813117-6.00002-8" @default.
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