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…
Los modelos ARIMA son muy útiles para pronosticar series temporales para datos univariable. Sin embargo, existe un tipo de modelo ARIMA que también puede considerar otras variables. Este tipo de modelo se llama ARIMAX, que significa en inglés “Auto-Regressive Integrated Moving Average with eXogenous variables” . ARIMAX es una extensión del modelo ARIMA tradicional Read more…
ARIMA models are very powerful for forecasting time series data when this data is univariate. However, there is a type of ARIMA model that can also consider other variables. This type of model is called ARIMAX, which stands for “Auto-Regressive Integrated Moving Average with eXogenous variables”. ARIMAX is an extension Read more…