Calculating Data Loss for Time-Series Data

Dimitri Bianco
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Abstract

Data transformations are commonly used across statistics to transform data distributions into distributions with properties that make them more user friendly. In time-series, stationarity is one of the most common assumptions that is violated because the mean and variance are time dependent. Dick and Fuller (1979) have proven that differencing data can make data stationary. It is also common to try to make data stationary through taking the natural log or using the growth rates of the data instead of the original non-stationary data. There is concern that transforming the data through differencing loses valuable information. This paper purposes a method for measuring data lost from these three types of transformations.
计算时间序列数据的数据丢失
数据转换通常用于跨统计数据将数据分布转换为具有用户更友好属性的分布。在时间序列中,平稳性是最常被违反的假设之一,因为平均值和方差是时间相关的。Dick和Fuller(1979)证明了差异数据可以使数据平稳。试图通过取自然对数或使用数据的增长率来代替原始非平稳数据来使数据平稳也很常见。有人担心,通过差异转换数据会丢失有价值的信息。本文提出了一种测量这三种转换所造成的数据损失的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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