Abstract
Abstract In the literature, vast amounts of methods of time-series modeling are described. Most for the methods, either classical or machine learning, left interpretation to the expert. Even though the interpretation is sometimes possible, usually, it is done only in a very narrow range of the applications. In the article approach to the extended time-series model interpretation is proposed. The algorithm of time-series model discovery in the form of the algebraic expression in a closed-form is described. The resulting algorithm utilizes the flexibility of the evolutionary optimization and possibility of the sparse regression to make concise models.
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