Multiple The right time Group fashion making use of Apache Trigger and Facebook Visionary

#datascience #machinelearning #timeseries

This video is part of Time Series playlist here –

One major challenge with time series in real world is dealing with multiple time series, Be it retailers who have millions of product and every product having different sales cycle or manufacturing industry dealing with hundreds of machinery. In such cases we need systems and solution that can help distribute time series model building across distributed nodes to enable high parallelism. In this video we will see how we can use facebook prophet to model and Apache Spark to distribute across multiple nodes

5 Antworten auf „Multiple The right time Group fashion making use of Apache Trigger and Facebook Visionary“

  1. Thanks for the video first of all.
    I would like to ask if I am using such models for timeseries analysis for financial data, how to constantly update the data and retrain the model to avoid data drift.

  2. Hi Srivatsan,

    I noticed one small issue in pandas udf code. We want to do sort values based on date column inside pandasudf as we do groupby. When we do groupby and apply pandasudf function, it will jumble the order of data insted of sequence data per ts.

  3. @AIEngineering, can you tell me, how to run multiple time series using SARIMAX or XGBoost with Pyspark? Can you please also recommend any literature about multiple time series forecasting ?

  4. Hello Sir I am working on Solar Energy Time Series(5 min granulity) where I have these night time values of energy as zero's. The values only appear during day time. I applied Facebook prophet models on such data the results are not getting better.If I remove those values prophet still won't give satisfactory results. Do you recommend any good time series model for such data?

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