Gaussian Process

Gaussian Process Imputation/Forecast Models

A well-established set of problems emerges when attempting to analyze non-stationary univariate time series (i.e., the signal’s mean and/or variance changes over time). A common approach is to impose some stationarity on the data so that certain modeling techniques can provide allow a research to make some predictions (e.g., ARIMA models). The selection of the appropriate assumptions to make when forcing a time series into stationarity is difficult to automate in many circumstances, requiring that a researcher evaluate competing models.

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