Discussing the article: "Causal analysis of time series using transfer entropy"
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Check out the new article: Causal analysis of time series using transfer entropy.
In this article, we discuss how statistical causality can be applied to identify predictive variables. We will explore the link between causality and transfer entropy, as well as present MQL5 code for detecting directional transfers of information between two variables.
Empirical data can be deceiving. Just because two variables seem to move in tandem does not mean one causes the other, which is why the saying "correlation is not causation" rings true. Correlation simply measures how connected two variables are, not why they are connected. For instance, imagine a strong correlation between ice cream sales and a stock price during the summer. This doesn't mean buying ice cream makes the stock go up! A more likely culprit is a hidden factor, like the season itself, affecting both variables independently. Similarly, a link between a company's stock and gold prices might exist, but the real cause could be something else entirely, like overall market sentiment or inflation influencing both prices. These examples highlight that correlated data can be misleading. They show a connection, but not the reason behind it. To truly understand if one thing causes another, we need more advanced tools.
The concept of causality, the notion that one event brings about another, is fundamental to scientific exploration. However, defining causality precisely presents a multifaceted challenge with deep philosophical, physical, and statistical considerations. Ideally, a cause would invariably produce a singular effect. However, isolating a single causal factor from the often complex web of influences impacting an outcome can be difficult. For instance, a surge in trading volume might correlate with a rise in stock price, but other factors, such as market sentiment and economic data releases, could also play a significant role. In such scenarios, researchers employ statistical techniques to infer causal relationships.
Author: Francis Dube