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!
@lewismambo80432 жыл бұрын
This Lecture in TMSA is very useful. Thank very much Prof.
@cagataymelan14074 жыл бұрын
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
@nicok33454 жыл бұрын
Thanks for this outstanding presentation :-).
@siabikebenezer5 жыл бұрын
Hello sir, can i please get the script for your presentation. I will really glad if you provide your codes to me. Thanks
@BrooklynBambi5 жыл бұрын
Share the source code please?
@apica12343 жыл бұрын
Could you please explain the process of generating IRFs and Variance decomposition in both methods
@PikkaKok4 жыл бұрын
At 20:18 aren't you inversing the diff with the same values you are trying to forecast? (... * series['beer'][-3:])
@mikiallen77334 жыл бұрын
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
@snivesz325 жыл бұрын
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.
@dagma34374 жыл бұрын
This perhaps? kzfaq.info/get/bejne/qrBdgpaZ0Jfagqs.html
@dagma34374 жыл бұрын
github.com/SimiY/pydata-sf-2016-arima-tutorial
@alexanderskusnov51194 жыл бұрын
Is there an example of Reinforcement Learning?
@WahranRai4 жыл бұрын
25:48 You forgot Water gate !
@Avinaash154 жыл бұрын
Could anyone explain the part where he puts the RMSE into context. Im not sure how that fits into forecasting future values
@dataEvo4 жыл бұрын
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.
@eytansuchard86403 жыл бұрын
How about using transformers ?
@jorjodimitrov5 жыл бұрын
yeah , put a link to github repository captain america. Scraping letter by letter from the video will take me a hole day.
@ImranKhan-fi2sm4 жыл бұрын
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??
@Human2023v14 жыл бұрын
apply a lead transformation of the forecasted series.