Holt Winters is just a fancy way of saying triple exponential forecasting. Exponential forecasting is supposedly easier to handle trends and seasonality. Holt Winters assume there is seasonality.
ARIMA can do seasonality too if you differentiate for it. I believe both require you to either decompose your time series and figure out if there is any seasonality in it. ARIMA requires that the time series be stationary so if it is not you need to transform it. Exponential smoothing including Holt Winters don't care and iirc you use exponential smoothing technique most of the time for nonstationary data.
GLM have rigorous assumptions for data that makes it usually harder for time series. Both ARIMA and Holt Winter are for forecasting where was GLM is for inferences. ARIMA is auto regressive where it regress on it's pass self. So if you look at the equation it's only Y's unless you use ARIMAX. GLM is a general framework for regression and it regress on other predictors (independent variables). It's tricky to do GLM on time series data because when you want to predict a value in the future say 2021 then often time you need to know your predictor values in the future to predict your response.
I know ARIMA inference is base on time lag. Hence regressing it past self. The relationship that your inferencing on is what response you care about and it's past self. So you can detect patterns in time lag. As for GLM you inference between the linear association between the independent predictors and the response.
ARIMA can do seasonality too if you differentiate for it. I believe both require you to either decompose your time series and figure out if there is any seasonality in it. ARIMA requires that the time series be stationary so if it is not you need to transform it. Exponential smoothing including Holt Winters don't care and iirc you use exponential smoothing technique most of the time for nonstationary data.
GLM have rigorous assumptions for data that makes it usually harder for time series. Both ARIMA and Holt Winter are for forecasting where was GLM is for inferences. ARIMA is auto regressive where it regress on it's pass self. So if you look at the equation it's only Y's unless you use ARIMAX. GLM is a general framework for regression and it regress on other predictors (independent variables). It's tricky to do GLM on time series data because when you want to predict a value in the future say 2021 then often time you need to know your predictor values in the future to predict your response.
I know ARIMA inference is base on time lag. Hence regressing it past self. The relationship that your inferencing on is what response you care about and it's past self. So you can detect patterns in time lag. As for GLM you inference between the linear association between the independent predictors and the response.