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This is particularly evident for nonlinear and time-dependent processes. We propose a novel dimension reduction technique, Average Volatility Dimensioning (AVD), which captures the behavior between features and reduces the multiple dimensions to a univariate signal. This article describes the developed mechanisms and compares this dimension reduction technique against nine state-of-the-art methods. The ability to retain the information of the data is evaluated by using the reduced dimensions for machine learning classification tasks and comparing the phase-space portraits. The validation is performed on eight different datasets featuring nonlinear time series data, covering domains such as movement recognition, fault detection, and environmental monitoring. 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