Thanks so much for a great presentation, Jeff Yau! I've been looking for techniques to model multivariate time series data, and found this video to be extremely helpful!
but the problem with sign autocorrelations are known to be non-linear more like XOR function which when we apply the vector autoregressions to it , will fail miserably ! so do you have any special advice as to which method works better with sign AND magnitude autocorrelations your input is highly appreciated
1) Has anyone found a link to Jeffrey Yau's hour-and-a-half version of this talk? 2) The description on this video is incorrect, this video is not about GDPR.
RMSE is on absolute units, which without context cannot tell by itself how good the model is. For instance, if RMSE is 100 when predicting values around 200, your % error is 50%. On the hand, if you are predicting values around 1.000.000, an RMSE of 100 is only 0.01% error. Therefore, just by looking at RMSE from two different scenarios you can't tell which one has a better fitted model.
Hii How to handle persistent model problem. While doing time series analysis i get the output which seems to be one time step ahead of the actual series. How to rectify this problem?? This thing i am getting with several ML, DL, and as well as with statistical algos. Please do reply??
Thanks so much for a great presentation, Jeff Yau! I've been looking for techniques to model multivariate time series data, and found this video to be extremely helpful!
This Lecture in TMSA is very useful. Thank very much Prof.
Very helpful. Thank you..! Just noticed that in 20:22 you are multiplying by lag 3 for inverse transformation although you differenced by lag 12
Hello sir, can i please get the script for your presentation. I will really glad if you provide your codes to me. Thanks
At 20:18 aren't you inversing the diff with the same values you are trying to forecast? (... * series['beer'][-3:])
but the problem with sign autocorrelations are known to be non-linear more like XOR function which when we apply the vector autoregressions to it , will fail miserably ! so do you have any special advice as to which method works better with sign AND magnitude autocorrelations
your input is highly appreciated
Share the source code please?
Thanks for this outstanding presentation :-).
Could you please explain the process of generating IRFs and Variance decomposition in both methods
1) Has anyone found a link to Jeffrey Yau's hour-and-a-half version of this talk?
2) The description on this video is incorrect, this video is not about GDPR.
This perhaps?
ruclips.net/video/tJ-O3hk1vRw/видео.html
github.com/SimiY/pydata-sf-2016-arima-tutorial
Is there an example of Reinforcement Learning?
Could anyone explain the part where he puts the RMSE into context. Im not sure how that fits into forecasting future values
RMSE is on absolute units, which without context cannot tell by itself how good the model is. For instance, if RMSE is 100 when predicting values around 200, your % error is 50%. On the hand, if you are predicting values around 1.000.000, an RMSE of 100 is only 0.01% error.
Therefore, just by looking at RMSE from two different scenarios you can't tell which one has a better fitted model.
25:48 You forgot Water gate !
Hii
How to handle persistent model problem. While doing time series analysis i get the output which seems to be one time step ahead of the actual series. How to rectify this problem?? This thing i am getting with several ML, DL, and as well as with statistical algos. Please do reply??
apply a lead transformation of the forecasted series.
How about using transformers ?
yeah , put a link to github repository captain america. Scraping letter by letter from the video will take me a hole day.