-
Notifications
You must be signed in to change notification settings - Fork 49
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Hello Signaflo, For one weekly time series it is taking (18-20)sec to compute the results. Is there any way to optimize the computation time. #24
Comments
Can you help me with this problem? |
@Sayan-Pal585 , are you available to share the data you're using? Have you tried running something similar in R? The problem is likely due to using the maximum likelihood method to optimize coefficients for weekly data leads to using large matrices in the internal Kalman Filter algorithm, which leads to much longer running times than if you were using monthly data. One potential quick fix is to use the CSS (conditional sum-of-squares) method, which if you have a lot of data, will give you results close to those obtained with maximum likelihood (ML). |
@signaflo I have attached the weekly time series. In R it is perfectly running fine. |
Time taken by various model parameters with different fitting strategy ML fitting strategy:->>>>>>>>>>>>>>>>>>>> CSSML fitting strategy:->>>>>>>>>>>>>>>>>>>> |
Is there any way that I can reduce the time for ML & CSS-ML fitting strategy.? |
I found that the optimizer function "BFGS" is taking more time. |
No description provided.
The text was updated successfully, but these errors were encountered: