Nonlinear autoregressive exogenous model
Okay kiddo, let's learn about a big phrase today! The nonlinear autoregressive exogenous model is a type of mathematical formula that helps us predict what will happen in the future by looking at what happened in the past.
Imagine you have a toy car that moves forward when you wind it up. You can predict how far it will go based on how many times you wound it up in the past. That's a simple form of prediction. Now, imagine that your toy car also speeds up or slows down on its own, and you have a windy day outside. Those factors affect how far the toy car will go too. But it's more complicated because you can't predict how much those external factors will change the toy car's movement.
The nonlinear autoregressive exogenous model is like a fancy equation that takes into account both the past and present unpredictable factors that affect a particular event, like how far the toy car will go.
It's called a nonlinear model because it doesn't just look at simple cause-and-effect relationships, like winding up the toy car. The model looks at complex relationships, where several factors affect the outcome, and you can't always predict how much each factor will matter.
That's where the term autoregressive comes in. It means that the model looks at what happened in the past to help predict the future. Like the toy car example, the model would use data on how fast the car moved when you wound it up or how much it changed when it hit a bump.
Finally, the exogenous part of the model takes into account external factors that might impact the event you're trying to predict. Like the windy day in the toy car example. You can't control the wind, and you can't always predict how much it will affect the toy car's movement.
So, that's the very basic explanation of the nonlinear autoregressive exogenous model - a fancy formula that takes into account all of the complicated factors that affect an event's outcome to predict what will happen in the future.
Imagine you have a toy car that moves forward when you wind it up. You can predict how far it will go based on how many times you wound it up in the past. That's a simple form of prediction. Now, imagine that your toy car also speeds up or slows down on its own, and you have a windy day outside. Those factors affect how far the toy car will go too. But it's more complicated because you can't predict how much those external factors will change the toy car's movement.
The nonlinear autoregressive exogenous model is like a fancy equation that takes into account both the past and present unpredictable factors that affect a particular event, like how far the toy car will go.
It's called a nonlinear model because it doesn't just look at simple cause-and-effect relationships, like winding up the toy car. The model looks at complex relationships, where several factors affect the outcome, and you can't always predict how much each factor will matter.
That's where the term autoregressive comes in. It means that the model looks at what happened in the past to help predict the future. Like the toy car example, the model would use data on how fast the car moved when you wound it up or how much it changed when it hit a bump.
Finally, the exogenous part of the model takes into account external factors that might impact the event you're trying to predict. Like the windy day in the toy car example. You can't control the wind, and you can't always predict how much it will affect the toy car's movement.
So, that's the very basic explanation of the nonlinear autoregressive exogenous model - a fancy formula that takes into account all of the complicated factors that affect an event's outcome to predict what will happen in the future.