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Agenda - Project Background & Members - Overview of In-House Time Series Prediction Tool - Achievements and Future Prospects

Project Members Lu Yan Data Scientist Terry So Machine Learning Engineer Yuan Yifan Machine Learning Engineer

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

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?

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

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

For non-DS For DS Project Background Tool Design

Agenda - Project Background & Members - Overview of In-House Time Series Prediction Tool - Achievements and Future Prospects

Main Components of the Tool Add Dataset Train the model Forecast Add Project Overview of In-House Time Series Auto Prediction Tool

Main Components of the Tool - Project Overview of Tool – for non-DS User

Main Components of the Tool Add Dataset Train the model Forecast Add Project Overview of In-House Time Series Auto Prediction Tool

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

Main Components of the Tool – Dataset Overview of Tool – for non-DS User

Main Components of the Tool Add Dataset Train the model Forecast Add Project Overview of In-House Time Series Auto Prediction Tool

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..)

Main Components of the Tool Add Dataset Train the model Forecast Add Project Overview of In-House Time Series Auto Prediction Tool

Overview of Tool – for non-DS User Main Components of the Tool - Forecast

- Runs Python client - Supports batch jobs - Provides advanced functions - Keeps adding new features Overview of Tool – for Data Scientist

Overview of Tool – for Data Scientist Batch Jobs

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

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

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

- 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

Overview of Tool – for Data Scientist Keep Adding New Feature - Show Past Predictions

Overview of Tool System Architecture

Overview of Tool System Architecture Django Framework – Web API

Overview of Tool System Architecture Celery – Distributed Task Queue

Overview of Tool System Architecture Elasticsearch - Log System

Agenda - Project Background & Members - Overview of In-House Time Series Prediction Tool - Achievements and Future Prospects

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

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

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

Thank you