Discussing the article: "Pattern Recognition Using Dynamic Time Warping in MQL5"
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Check out the new article: Pattern Recognition Using Dynamic Time Warping in MQL5.
In this article, we discuss the concept of dynamic time warping as a means of identifying predictive patterns in financial time series. We will look into how it works as well as present its implementation in pure MQL5.
Dynamic time warping is a sophisticated algorithm designed to measure the similarity between two sequences of data that evolve over time, even when their speeds or rhythms vary. Unlike traditional methods that require strict alignment between data points, DTW offers a more flexible approach by allowing for warping or stretching of time to find the optimal match between the sequences. Imagine two people walking through a forest on different paths. They both start at the same place and end at the same place, but one might walk faster than the other and make arbitrary stops for whatever reason. DTW helps figure out the best way to match the steps of both, even though they took different paths. DTW can effectively account for differences in walking speed, acceleration, or deceleration, providing a measure of similarity. This versatility makes it applicable to a wide range of data types, including audio, video, and graphics. Any data that can be transformed into a sequential format is a potential candidate for DTW analysis.
Author: Francis Dube