Conditional Normalization in Time Series Analysis

Puwasala Gamakumara (puwasala.gamakumara@gmail.com), Edgar Santos-Fernandez (edgar.santosfernandez@qut.edu.au), Priyanga Talagala (priyangad@uom.lk), Rob Hyndman, Kerrie Mengersen (k.mengersen@qut.edu.au) and Catherine Leigh (catherine.leigh@rmit.edu.au)

No 10/23, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Time series often reflect variation associated with other related variables. Controlling for the effect of these variables is useful when modeling or analysing the time series. We introduce a novel approach to normalize time series data conditional on a set of covariates. We do this by modeling the conditional mean and the conditional variance of the time series with generalized additive models using a set of covariates. The conditional mean and variance are then used to normalize the time series. We illustrate the use of conditionally normalized series using two applications involving river network data. First, we show how these normalized time series can be used to impute missing values in the data. Second, we show how the normalized series can be used to estimate the conditional autocorrelation function and conditional cross-correlation functions via additive models. Finally we use the conditional cross-correlations to estimate the time it takes water to flow between two locations in a river network.

Keywords: conditional normalization; missing value imputation; conditional autocorrelation; conditional cross-correlation; lag time estimation; stream data; water quality (search for similar items in EconPapers)
Pages: 36
Date: 2023
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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