아마존닷컴처럼 Amazon Forecast로 시계열 예측하기 - 강지양 솔루션즈 아키텍트, AWS / 강태욱 매니저, GSSHOP :: AWS Summit Seoul 2019
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The document provides an overview of Amazon Forecast, a service for time-series forecasting that allows users to predict product demand, financial metrics, and workforce needs without requiring machine learning expertise. It describes various features and predefined recipes available for model creation, aiming to address challenges associated with historical data limitations and external factors. Best practices for data preparation and model optimization are also emphasized to improve forecasting accuracy.
![© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Next Step
• Amazon Forecast (Preview)
https://aws.amazon.com/forecast/
• Amazon Forecast Documentation (Preview)
https://docs.aws.amazon.com/forecast/
• 실습 URL (Jupyter Notebook)
https://github.com/aws-samples/amazon-forecast-samples
• AWS re:Invent 2018: [NEW LAUNCH!] Introducing Amazon Forecast (AIM344)
https://youtu.be/kU5cFdXjwOc
• AWS Online Tech Talks: Build Forecasts and Individualized Recommendations with AI
https://youtu.be/glSFmuAfRjE](https://image.slidesharecdn.com/day2track6session2-190419042513/85/Amazon-Forecast-AWS-GSSHOP-AWS-Summit-Seoul-2019-47-320.jpg)
아마존닷컴처럼 Amazon Forecast로 시계열 예측하기 - 강지양 솔루션즈 아키텍트, AWS / 강태욱 매니저, GSSHOP :: AWS Summit Seoul 2019
- 1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 아마존닷컴처럼 Amazon Forecast로 시계열 예측하기 강지양 솔루션즈아키텍트 AWS S e s s i o n I D 강태욱 매니저 GSSHOP
- 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Forecast 소개 시계열 예측에 대해 궁금한 것들 적용 사례: GSSHOP 모범 사례 목차
- 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 반복되는 고객의 요청... “아마존닷컴의 머신 러닝 기술을 우리가 사용할 방법이 있을까요?”
- 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Time-Series Forecasting 시계열 예측 상품 수요 예측 재무지표 예측 노동력 수요 예측 재고 관리 예측
- 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 외부 요인 날씨, 휴일, 이벤트, 유행 트렌드를 반영하려면 과거를 모르는 데이터 이전 데이터가 없는 새로운 제품을 출시했다면 기타 변수 다양한 제품 속성과 관련 데이터를 고려하려면 튀거나 간헐적인 데이터 불규칙한 패턴의 현실 데이터를 예측한다면 정확한 예측은 매우 어렵습니다
- 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 기존의 문제 접근 방식 Traditional ARIMA Deep Learning RNN Regression XGBoost 하이퍼파라미터 최적화 문제 메타데이터 반영 문제 데이터의 연속성 반영 문제
- 7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Forecast Amazon.com의 기술로 만든 정확도 높은 시계열 예측 서비스 ML 전문가가 아니라도 문제 없습니다.
- 8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 아마존닷컴의 기술로 어려웠던 예측 문제를 해결 급격한 변화 예측 이전 데이터가 없는 새로운 제품의 수요 예측 연관된 시계열 데이터들간의 관계 학습 외부 요인 반영 (계절, 공휴일, 할인 행사 등)
- 9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 데이터 파이프라인 구성 모델 배포 다섯번 클릭으로 만드는 커스텀 딥러닝 모델 모델들의 정확도 메트릭 비교 AutoML 또는 Amazon Forecast 제공 알고리즘 중에서 선택 예측 실행
- 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Forecast의 동작 방식 Amazon Forecast
- 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Forecast Predefined Recipes The DeepAR+ Recipe The Multi-Quantile Recurrent Neural Network (MQRNN) Recipe (NIPS 2017) The Spline Quantile Forecaster (SQF) Recipe The Non-Parametric Time Series (NPTS) Recipe The Mixture Density Networks (MDN) Recipe The ARIMA Recipe The Exponential Smoothing (ETS) Recipe The Prophet Recipe 머신러닝 경험 없이도 예측 시스템을 만들 수 있도록 미리 만들어진 레시피를 제공합니다.
- 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 다양한 예측 문제에 적용
- 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 한눈에 보는 시계열 예측
- 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 한눈에 보는 시계열 예측
- 16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. https://en.wikipedia.org/wiki/Probabilistic_forecasting
- 18. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 확률 예측이란? (μ, σ)
- 19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 중요한 이유
- 20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 확률 예측의 결과값 P10, P50, P90: “Quantiles”
- 23. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 강태욱 매니저 GSSHOP
- 24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 강태욱 매니저 | 물류본부
- 25. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 디지털 트랜스포메이션
- 26. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. DevOps AWS Cloud Hybrid Cloud Micro Service Architecture IDC Monolithic Optimized
- 27. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 배송주문 물류센터 물류 관점 현황/지표 Data Driven Decision Making
- 28. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 물동량 예측 물류/배송 효율화 Amazon SageMaker Amazon Forecast
- 29. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Forecast (Preview) Predefined Dataset Domain Domain Dataset Recipes 시작하기 – 공식 문서, 권한 설정, Github 샘플 데이터 & 코드 PoC 결론
- 30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Predefined Dataset Domain
- 31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Retail Domain 데이터 준비 TV쇼핑 상품의 2013년 ~ 현재 • TARGET_TIME_SERIES : 상품아이디, TIMESTAMP, 출고량 • RELATED_TIME_SERIES : 상품아이디, TIMESTAMP, 방송여부 (0, 1) • ITEM_METADATA Dataset : 상품아이디, TIMESTAMP, 브랜드, 색상
- 32. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. RELATED_TIME_SERIES DATA (retail domain) HolidayCalendarName
- 33. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Recipes
- 34. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. PoC
- 35. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. PoC
- 36. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. SDK S3 Amazon Forecast Aurora PoC 시스템 recipe train model EC2물류대시보드
- 37. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. POC 결과
- 38. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. POC 결과
- 39. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. POC 결과
- 40. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Forecast 결론 8 hours
- 41. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 42. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 모범 사례 빅데이터의 힘을 믿으세요 데이터가 많을수록 정확합니다 모델을 너무 나누지 마세요 관련 있는 시계열 데이터들의 공통 모델을 만드세요 TARGET 데이터로 시작하세요 보조 데이터는 주인공이 아닙니다 시계열 데이터를 자르지 마세요 끊기지 않은 전체 데이터가 필요합니다
- 43. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 데이터가 많을수록 정확합니다 Scaling to Very Very Large Corpora for Natural Language Disambiguation, Banko and Brill, Microsoft Research (2001) http://www.aclweb.org/anthology/P01-1005 “These results suggest that we may want to reconsider the trade-off between spending time and money on algorithm development versus spending it on corpus development.” 알고리즘 선정보다 학습 데이터의 양이 더 중요
- 44. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 45. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 데이터 머신러닝 도메인 지식 균형
- 46. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Forecast 개발자도 쉽게 접근할 수 있는 시계열 예측 주 요 기 능 높은 정확도 자동 최적화 통합 모델링콜드 스타트 자동 재학습
- 47. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Next Step • Amazon Forecast (Preview) https://aws.amazon.com/forecast/ • Amazon Forecast Documentation (Preview) https://docs.aws.amazon.com/forecast/ • 실습 URL (Jupyter Notebook) https://github.com/aws-samples/amazon-forecast-samples • AWS re:Invent 2018: [NEW LAUNCH!] Introducing Amazon Forecast (AIM344) https://youtu.be/kU5cFdXjwOc • AWS Online Tech Talks: Build Forecasts and Individualized Recommendations with AI https://youtu.be/glSFmuAfRjE