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Check out the new article: Dynamic mode decomposition applied to univariate time series in MQL5.

Dynamic mode decomposition (DMD) is a technique usually applied to high-dimensional datasets. In this article, we demonstrate the application of DMD on univariate time series, showing its ability to characterize a series as well as make forecasts. In doing so, we will investigate MQL5's built-in implementation of dynamic mode decomposition, paying particular attention to the new matrix method, DynamicModeDecomposition().

Dynamic Mode Decomposition (DMD) is a technique used to analyze complex dynamic systems. Engineers use it in fluid flow analysis to extract spatiotemporal structures from complex datasets. DMD works by breaking down a system's data into simpler representations called modes. Each mode represents a distinct spatial pattern with its own oscillation frequency and a growth or decay rate. This allows engineers to analyze a fluid system's underlying dynamics without needing to solve the often-difficult governing equations.

Spatiotemporal structures are patterns that maintain their shape and identity over time and space. Imagine a smoke ring floating through the air. The smoke ring is a structure that holds its form and moves as a single unit, even as the air around it swirls.

Engineers use techniques like DMD to identify and analyze such structures within complex fluid flows. In this article we explore MetaTrader 5's implementation of DMD. Through practical code demonstrations, readers will learn how to use the new matrix method, DynamicModeDecomposition(). We will discuss its inputs, as well as present essential code utilities needed for processing its output. 

Author: Francis Dube