Bukalapak Deep Dive Into Facebook Prophet Meetup Bukalapak X Komunitas R Indonesia X Machine Learning ID Bukalapak R&D Office, Bandung | 28-June-2019 Deep Dive into Facebook Prophet

Fiqry Revadiansyah Data Scientist @Bukalapak Bukalapak Meetup user! Indonesia Experiences Data Scientist @Bukalapak – Shoping Experience Tribe, Squad Chat and New Users Technical Content Reviewer @Packt Publishing – Time Series Analytics Expert Author of GSTAR (Generalized Space Time Autoregressive) package in R Education BS, Statistics @Universitas Padjadjaran Research Interest: Time Series Analysis, Regression Modeling, Spatial Modeling, Machine Learning, Gamification Business Meetup Bukalapak X Komunitas R Indonesia X Machine Learning ID @Bukalapak R&D Office, Bandung | 28-June-2019 About me

Disclaimer Bukalapak Deep Dive Into Facebook Prophet

Session Info NO MATH/STAT FORMULA 01 ASK NOW OR NEVER 02 PROACTIVE REWARD 03 No need to have a math/stat background to hype this topic! *Unless you ask Don’t hesitate, no hassle, just ask at your own moment! Ensure yourself fun, comfort, and proactively share your thoughts, then get the reward Bukalapak Deep Dive Into Facebook Prophet

Rewards Bukalapak Deep Dive Into Facebook Prophet Just for 3 best learners! *This e-book worth $26 (Amazon Kindle)

List of Contents 1. Background 2. Model 3. Workshop Bukalapak Deep Dive Into Facebook Prophet

Forecasting is an art of 2C: Coincidence and Confidence Bukalapak Background: Time Series History

Forecasting History Why Bukalapak Forecasting jaman old Forecasting jaman now From 250 – 900 AD until now, forecasting is still exist. It is mostly because forecasting technique require highest quality of art and human confidence Background: Time Series History

1970’ – 2000’ ARIMA (Box & Jenkins, 1970) ARCH/GARCH (Engle 1982) State Space Model (Snyder, 1985) Artificial Neural Network (Zhang, 1998) Now, what is going on? 1950’ – 1970’ Exponential Smoothing (Brown, 1959) Holt Winter SES (Holt, Winter, 1960) Decomposition (Brown, 1963) 2000’ - Now Spatio-Temporal Time Series Nonlinear Time Series Machine Learning Time Series Facebook Prophet Model Bukalapak Background: Time Series History

Time Series Forecasting In a Real Practice Industry Bukalapak Background II: Time Series in Real Practice How if….

“Internet Cepat Peramalan Tepat? Buat apa?" Bukalapak - Menteri Time Series dan Forecasting (2018 – 2016 SM) Background II: Time Series in Real Practice

Myths - Useless - Cannot fit into production - Math and Stats Geek, not linear to business matter Bukalapak Time Series Forecasting Myth vs Fact Facts - Performance Measurement - Maintainable and able to be deployed - Capable to solve business problem Background II: Time Series in Real Practice

Facebook Prophet Model Bukalapak FB Prophet Model

Bukalapak FB Prophet Model Facebook Prophet Model *Forecasting at Scale, Taylor, S.J., and Letham, B. (2017) Time Series Model Founded to make automation of data forecasting for coping with the discrepancy between domain-based knowledge and statistical knowledge A Brief Preview

FB Prophet Baseline Decomposition-Based 01 Feature of Business Time Series 02 Analyst-in-the-loop 03 FB team believes that a time- series data is containing various components, such as trend, seasonality, holiday, etc. Compatible to business day, such as feature deployment date, national day-off, etc. Automate the process of surface problems, visually inspect forecasts, modelling, and evaluation. Bukalapak FB Prophet Model

Bukalapak FB Prophet Model Why should FB Prophet? No Math/Stat Background Required Easy to Customize Long lasted Forecast Cited by Many Journals

Workshop Time Bukalapak Miniworkshop: FB Prophet

Bukalapak Closing Statement

Thank you Fiqry Revadiansyah Data Scientist @Bukalapak @fiqryr Bukalapak Meetup user! Indonesia Meetup Bukalapak X Komunitas R Indonesia X Machine Learning ID Bukalapak R&D Office, Bandung | 28-June-2019 mrboneclinkz