In the last article, we learned how to train a Machine Learning model like Linear Regression or XGBoost to forecast Time Series data. We had to reframe the dataframe as a supervised learning problem. You can read about this process here. To explain the process we used Forex data, specifically Read more…
In a previous article, we introduced Vector Auto-Regression (VAR), a statistical model designed for multivariate time series analysis and forecasting. VAR provides a robust solution by effectively capturing dynamic relationships between multiple variables over time. In this article, we will train a VAR model step-by-step. We will use the dataset Read more…
We talked about Vector Autorregression or VAR in a previous article. But, does it really make sense to use two different variables to get a forecast? The answer is no, not always at least. It will only be beneficial if there is some kind of relationship between them. Using unrelated Read more…
There are times when we need to forecast several variables at the same time. For these occasions, traditional methods such as ARIMA or Exponential Smoothing are not sufficient since they are univariate methods. Vector AutoRegression (VAR) is a statistical model for multivariate time series analysis and forecasting. It is used Read more…