Discussing the article: "Data Science and ML (Part 44): Forex OHLC Time series Forecasting using Vector Autoregression (VAR)"

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Check out the new article: Data Science and ML (Part 44): Forex OHLC Time series Forecasting using Vector Autoregression (VAR).

Explore how Vector Autoregression (VAR) models can forecast Forex OHLC (Open, High, Low, and Close) time series data. This article covers VAR implementation, model training, and real-time forecasting in MetaTrader 5, helping traders analyze interdependent currency movements and improve their trading strategies.

This is a traditional and statistical time series forecasting tool used to investigate the dynamic relationships between multiple time series variables. Unlike univariate autoregressive models such as ARIMA (discussed in the previous article) which only forecast a single variable based on its previous values, VAR models investigate the interconnectivity of many variables.

They accomplish this by modeling each variable as a function of not only its previous values but also of the past values of other variables in the system. In this article, we will explore the fundamentals of Vectorautoregression and its application to trading.

Vector Autoregression was first presented in the 1960s by economist Clive Granger. Granger's significant discoveries laid the framework for understanding and modeling the dynamic interactions that exist among economic factors. VAR models acquired significant momentum in econometrics and macroeconomics during the 1970s and 1980s.

This technique is a multivariate extension of auto-regression (AR) models. While traditional AR models such as ARIMA, analyze the relationship between a single variable and its lagged values, VAR models consider multiple variables simultaneously. In a VAR model, each variable is regressed on its own lagged values as well as the lagged values of other variables in the system.

Author: Omega J Msigwa