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MLaz
MLaz
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ARIMA is for a univariate time series. You would look at how well-being changes over time, given the previous values of well-being. There is no opportunity to include covariates in an ARIMA model.

For regression, ARIMA models are therefore not the answer. However ARIMA models can have covariates added, these models are called ARIMAX (Autoregressive integrated moving average with exogenous predictors) models. You can also have regression models with time series errors, dynamic generalised linear models. I would have a look at ARIMAX models online as I don't think they are massively more complicated than the standard ARIMA models.

Forgetting regression and talking about your confusion about having multiple cities, ARIMA models are univariate and so not appropriate for multiple cities either. The extension to multivariate time series is called VARIMA, where the V stands for vector and is for when you can put your multiple time series into a vector.

Both of the above are extensions, to the standard ARIMA model and, therefore, are somewhat more difficult, than standard ARIMA models. Combining both ideas is significantly more difficult still. Your idea for averaging over all cities might be a good idea, although obviously not ideal. You could then attempt an ARIMAX model given your averages. You can always look at just averaged well-being on its own first and fit an ARIMA model if you are completely new to ARIMA and want to make sure you are comfortable with the ARIMA model first.

ARIMA is for a univariate time series. You would look at how well-being changes over time, given the previous values of well-being. There is no opportunity to include covariates in an ARIMA model.

For regression, ARIMA models are therefore not the answer. However ARIMA models can have covariates added, these models are called ARIMAX (Autoregressive integrated moving average with exogenous predictors) models. You can also have regression models with time series errors, dynamic generalised linear models. I would have a look at ARIMAX models online as I don't think they are massively more complicated than the standard ARIMA models.

Forgetting regression and talking about your confusion about having multiple cities, ARIMA models are univariate and so not appropriate for multiple cities either. The extension to multivariate time series is called VARIMA, where the V stands for vector and is for when you can put your multiple time series into a vector.

Both of the above are extensions, and therefore somewhat more difficult, than standard ARIMA models. Combining both ideas significantly more difficult still. Your idea for averaging over all cities might be a good idea, although obviously not ideal. You could then attempt an ARIMAX model given your averages. You can always look at just averaged well-being on its own first and fit an ARIMA model if you are completely new to ARIMA and want to make sure you are comfortable with the ARIMA model first.

ARIMA is for a univariate time series. You would look at how well-being changes over time, given the previous values of well-being. There is no opportunity to include covariates in an ARIMA model.

For regression, ARIMA models are therefore not the answer. However ARIMA models can have covariates added, these models are called ARIMAX (Autoregressive integrated moving average with exogenous predictors) models. You can also have regression models with time series errors, dynamic generalised linear models. I would have a look at ARIMAX models online as I don't think they are massively more complicated than the standard ARIMA models.

Forgetting regression and talking about your confusion about having multiple cities, ARIMA models are univariate and so not appropriate for multiple cities either. The extension to multivariate time series is called VARIMA, where the V stands for vector and is for when you can put your multiple time series into a vector.

Both of the above are extensions to the standard ARIMA model and, therefore, are somewhat more difficult than standard ARIMA models. Combining both ideas is significantly more difficult still. Your idea for averaging over all cities might be a good idea, although obviously not ideal. You could then attempt an ARIMAX model given your averages. You can always look at just averaged well-being on its own first and fit an ARIMA model if you are completely new to ARIMA and want to make sure you are comfortable with the ARIMA model first.

Source Link
MLaz
MLaz
  • 483
  • 1
  • 4
  • 10

ARIMA is for a univariate time series. You would look at how well-being changes over time, given the previous values of well-being. There is no opportunity to include covariates in an ARIMA model.

For regression, ARIMA models are therefore not the answer. However ARIMA models can have covariates added, these models are called ARIMAX (Autoregressive integrated moving average with exogenous predictors) models. You can also have regression models with time series errors, dynamic generalised linear models. I would have a look at ARIMAX models online as I don't think they are massively more complicated than the standard ARIMA models.

Forgetting regression and talking about your confusion about having multiple cities, ARIMA models are univariate and so not appropriate for multiple cities either. The extension to multivariate time series is called VARIMA, where the V stands for vector and is for when you can put your multiple time series into a vector.

Both of the above are extensions, and therefore somewhat more difficult, than standard ARIMA models. Combining both ideas significantly more difficult still. Your idea for averaging over all cities might be a good idea, although obviously not ideal. You could then attempt an ARIMAX model given your averages. You can always look at just averaged well-being on its own first and fit an ARIMA model if you are completely new to ARIMA and want to make sure you are comfortable with the ARIMA model first.