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!
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.
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
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??
