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Time Series Data Prediction for Service Ops

Time Series Data Prediction for Service Ops

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LINE DEVDAY 2021
PRO

November 11, 2021
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Transcript

  1. None
  2. Agenda - Project Background & Members - Overview of In-House

    Time Series Prediction Tool - Achievements and Future Prospects
  3. Project Members Lu Yan Data Scientist Terry So Machine Learning

    Engineer Yuan Yifan Machine Learning Engineer
  4. Typical use case for user inquiry form à User buys

    new iPhone à Has problem transferring LINE account to the new device à Contact LINE via an inquiry form Project Background
  5. Project Background a Typical Use Case LINE Fukuoka Data Scientist

    Team + Value Management Center < Inquiry Count Forecasting> How many users’ inquiries do we need to treat for each LINE service every month? Every day? Every hour?
  6. Project Background Motivation for Developing a Forecasting Tool - Dozens

    of predictions required every month for different departments - High project communication cost - Limited number of Data Scientists - Relatively similar data structure - Many repetitive operations of data pre-treatment and model training
  7. Project Background - Develop a tool for both Data Scientists

    and non-Data Scientist users from LINE biz/operational service department - For non-DS(non-Data Scientist) users - Let users do the forecast by themselves - For DS(Data Scientist) - Automate repetitive steps Motivation for Developing a Forecasting Tool
  8. For non-DS For DS Project Background Tool Design

  9. Agenda - Project Background & Members - Overview of In-House

    Time Series Prediction Tool - Achievements and Future Prospects
  10. Main Components of the Tool Add Dataset Train the model

    Forecast Add Project Overview of In-House Time Series Auto Prediction Tool
  11. Main Components of the Tool - Project Overview of Tool

    – for non-DS User
  12. Main Components of the Tool Add Dataset Train the model

    Forecast Add Project Overview of In-House Time Series Auto Prediction Tool
  13. Main Components of the Tool – Dataset Overview of Tool

    – for non-DS User - Supports different kind of data sources (CSV, MySQL, Presto, Spark SQL etc.) - Connects to internal DB - Provides anomaly detection and imputation function
  14. Main Components of the Tool – Dataset Overview of Tool

    – for non-DS User
  15. Main Components of the Tool Add Dataset Train the model

    Forecast Add Project Overview of In-House Time Series Auto Prediction Tool
  16. Main Components of the Tool - Predictor Overview of Tool

    – for non-DS User - Different types of models (Prophet, LSTM, ARIMA, Temporal Fusion Transformers etc..) - Hyperparameter Tuning - Model Explanation - Model Evaluation (RMSE/MAPE/WAPE etc..)
  17. Main Components of the Tool Add Dataset Train the model

    Forecast Add Project Overview of In-House Time Series Auto Prediction Tool
  18. Overview of Tool – for non-DS User Main Components of

    the Tool - Forecast
  19. - Runs Python client - Supports batch jobs - Provides

    advanced functions - Keeps adding new features Overview of Tool – for Data Scientist
  20. Overview of Tool – for Data Scientist Batch Jobs

  21. Overview of Tool – for Data Scientist Advanced Functions Examples

    – Hyperparameter Tuner
  22. Overview of Tool – for Data Scientist Advanced Functions Examples

    – Model Explanation
  23. Overview of Tool – for Data Scientist Advanced Functions Examples

    – Model Explanation
  24. - Support usage of standard SQL queries to run the

    model and to do the prediction Overview of Tool – for Data Scientist Keep Adding New Features – SQL-Like Syntax
  25. Overview of Tool – for Data Scientist Keep Adding New

    Feature - Show Past Predictions
  26. Overview of Tool System Architecture

  27. Overview of Tool System Architecture Django Framework – Web API

  28. Overview of Tool System Architecture Celery – Distributed Task Queue

  29. Overview of Tool System Architecture Elasticsearch - Log System

  30. Agenda - Project Background & Members - Overview of In-House

    Time Series Prediction Tool - Achievements and Future Prospects
  31. Achievements and Future Prospects Quantity of prediction tasks + High

    project communication cost VS Limited number of Data Scientists Repetitive operations of data pre- treatment and model training DS can take advantage of the API & advanced functions and can run batch jobs easily Non-DS Users can do predictions by themselves with the UI and functionalities developed Use Case with Value Management Center
  32. Achievements and Future Prospects Use Case with Value Management Center

    - Use case – Inquiry Count Forecasting - After the usage of this tool, the work time has been reduced by 83% - The accuracy of prediction is higher and more stable, regardless of seasonality and personnel change. Source :https://linefukuoka.blog.jp/archives/20210630_01_news_plan_and_op.html
  33. Achievements and Future Prospects - Open to more business departments

    - Improve UI - Support more types of data visualization - Allow users to select weather/holiday common features - Support multi-horizon forecast Future Prospects
  34. Thank you