By Megan Czasonis, Mark Kritzman, and David Turkington
We show that relevance-based prediction offers an elegant solution to the problem of incomplete data, preserving valuable information and enhancing prediction reliability in a way that is not possible using traditional models.
When setting out to form data-driven predictions, it’s common to encounter incomplete information, such as a time series with shorter history lengths or observations with missing data. Traditional methods for addressing this challenge either discard valuable data or manufacture replacements based on limiting assumptions, leading to unreliable results. We propose a novel technique called Relevance-Based Prediction (RBP), which elegantly navigates the pitfalls of missing data by retaining more information and accounting for the relative importance of observations for which only partial data is available. We show that RBP offers an elegant solution to the problem of incomplete data, preserving valuable information and enhancing prediction reliability in a way that is not possible using traditional models.
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