ARX is a statistical model used in the analysis of time series and in the prediction of dynamic variables. ARX is an acronym for "AutoRegressive model with eXogenous inputs".
The ARX model is an extension of the autoregressive (AR) model that incorporates exogenous variables (X) to model the relationship between a variable of interest and other explanatory variables. The ARX model is useful when future values of the variable of interest may depend on past values of the same variable, as well as on past values of other related variables.
In practice, the ARX model can be fitted to the data by identifying the AR and X parameters that best describe the time series. The fitted model can then be used to make future predictions or to analyse the relationship between the variable of interest and exogenous variables.
The ARX model is a simpler model than the ARMAX model, as it only considers the relationship between the variable of interest and the exogenous variables through an autoregressive term. However, the ARX model is still useful in many cases where the inclusion of moving average terms or more than one exogenous variable is not necessary or not possible.
If you don't know the difference between an ERP (Enterprise Resource Planning) system and a CRM (Customer Relationship Management) system, here's what you need to know about the [...]
Read More »If you've ever wondered how Spotify recommends songs you like or how Siri and Alexa can understand what you say to them... the answer is that you can [...]
Read More »AI technologies are currently being used in companies to transform business processes, boost customer interaction and improve customer service.
Read More »The Big Data market is booming. Although the need to transform data into information for decision making is not new, the need to [...]
Read More »
