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Show HN: Time-series Forecasting with Exponential Smoothing State Space Model(github.io)

1 point by tsforecaster 1 year ago flag hide 11 comments

john_doe 1 year ago next
Great post! Exponential smoothing state space models are a powerful tool for time-series forecasting.
- user2 1 year ago next
  I agree! I've used this method in the past with great success. How do you handle exogenous variables in your model?
  john_doe 1 year ago next
  Excellent question! I typically include them as part of the state vector, allowing the model to learn the relationship between the exogenous variables and the response variable.
user3 1 year ago prev next
I've heard that these models can have difficulty handling seasonality. Do you have any tips for dealing with seasonal data?
- john_doe 1 year ago next
  That's a common misconception. In fact, exponential smoothing state space models can handle seasonality quite well, especially when combined with differencing. I typically use a seasonal difference of order one to account for seasonality in my models.
user4 1 year ago prev next
Have you compared this method to more complex models like LSTM or GRU?
- john_doe 1 year ago next
  I have, and I've found that exponential smoothing state space models often perform just as well, if not better, than more complex deep learning models for time-series forecasting. Plus, they're much easier to interpret and debug!
user5 1 year ago prev next
What tools or libraries do you use to implement these models?
- john_doe 1 year ago next
  I'm a big fan of the `forecast` package in R, which provides a user-friendly interface for building and evaluating exponential smoothing state space models. But there are also great libraries for Python, such as `pmdarima` and ` Prophet`.
user6 1 year ago prev next
Can you explain the intuition behind exponential smoothing? Why do we use exponential weights instead of fixed weights?
- john_doe 1 year ago next
  Exponential smoothing is a way to update our estimate of the mean response variable over time, taking into account new observations as they become available. We use exponential weights because they allow us to give more weight to recent observations, while still incorporating information from earlier observations. This is particularly useful in time-series forecasting, where recent observations are often more informative than older observations.

john_doe 1 year ago next
Great post! Exponential smoothing state space models are a powerful tool for time-series forecasting.
- user2 1 year ago next
  I agree! I've used this method in the past with great success. How do you handle exogenous variables in your model?
  john_doe 1 year ago next
  Excellent question! I typically include them as part of the state vector, allowing the model to learn the relationship between the exogenous variables and the response variable.
user3 1 year ago prev next
I've heard that these models can have difficulty handling seasonality. Do you have any tips for dealing with seasonal data?
- john_doe 1 year ago next
  That's a common misconception. In fact, exponential smoothing state space models can handle seasonality quite well, especially when combined with differencing. I typically use a seasonal difference of order one to account for seasonality in my models.
user4 1 year ago prev next
Have you compared this method to more complex models like LSTM or GRU?
- john_doe 1 year ago next
  I have, and I've found that exponential smoothing state space models often perform just as well, if not better, than more complex deep learning models for time-series forecasting. Plus, they're much easier to interpret and debug!
user5 1 year ago prev next
What tools or libraries do you use to implement these models?
- john_doe 1 year ago next
  I'm a big fan of the `forecast` package in R, which provides a user-friendly interface for building and evaluating exponential smoothing state space models. But there are also great libraries for Python, such as `pmdarima` and ` Prophet`.
user6 1 year ago prev next
Can you explain the intuition behind exponential smoothing? Why do we use exponential weights instead of fixed weights?
- john_doe 1 year ago next
  Exponential smoothing is a way to update our estimate of the mean response variable over time, taking into account new observations as they become available. We use exponential weights because they allow us to give more weight to recent observations, while still incorporating information from earlier observations. This is particularly useful in time-series forecasting, where recent observations are often more informative than older observations.