Time Series Forecasting Using Recurrent Neural Network and Vector Autoregressive Model: When and How

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  • Опубликовано: 29 окт 2024

Комментарии • 20

  • @michaelcao9483
    @michaelcao9483 4 года назад +9

    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!

  • @lewismambo8043
    @lewismambo8043 2 года назад

    This Lecture in TMSA is very useful. Thank very much Prof.

  • @cagataymelan1407
    @cagataymelan1407 4 года назад

    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

  • @siabikebenezer
    @siabikebenezer 5 лет назад +22

    Hello sir, can i please get the script for your presentation. I will really glad if you provide your codes to me. Thanks

  • @PikkaKok
    @PikkaKok 4 года назад +3

    At 20:18 aren't you inversing the diff with the same values you are trying to forecast? (... * series['beer'][-3:])

  • @mikiallen7733
    @mikiallen7733 4 года назад

    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

  • @BrooklynBambi
    @BrooklynBambi 5 лет назад +8

    Share the source code please?

  • @nicok3345
    @nicok3345 4 года назад +1

    Thanks for this outstanding presentation :-).

  • @apica1234
    @apica1234 3 года назад

    Could you please explain the process of generating IRFs and Variance decomposition in both methods

  • @snivesz32
    @snivesz32 5 лет назад +5

    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.

    • @dagma3437
      @dagma3437 4 года назад +1

      This perhaps?
      ruclips.net/video/tJ-O3hk1vRw/видео.html

    • @dagma3437
      @dagma3437 4 года назад +1

      github.com/SimiY/pydata-sf-2016-arima-tutorial

  • @alexanderskusnov5119
    @alexanderskusnov5119 4 года назад

    Is there an example of Reinforcement Learning?

  • @Avinaash15
    @Avinaash15 4 года назад

    Could anyone explain the part where he puts the RMSE into context. Im not sure how that fits into forecasting future values

    • @dataEvo
      @dataEvo 4 года назад +4

      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.

  • @WahranRai
    @WahranRai 4 года назад +1

    25:48 You forgot Water gate !

  • @ImranKhan-fi2sm
    @ImranKhan-fi2sm 5 лет назад

    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??

    • @Human2023v1
      @Human2023v1 4 года назад

      apply a lead transformation of the forecasted series.

  • @eytansuchard8640
    @eytansuchard8640 3 года назад

    How about using transformers ?

  • @jorjodimitrov
    @jorjodimitrov 5 лет назад +12

    yeah , put a link to github repository captain america. Scraping letter by letter from the video will take me a hole day.