Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Prophetを使った時系列データ予測と機械学習モデルとの比較 / prophet-vs-ml
Search
Haruki Okuyama
October 27, 2019
Business
0
1.3k
Prophetを使った時系列データ予測と機械学習モデルとの比較 / prophet-vs-ml
時系列データにおいて, Prophetと3時間で作成した機械学習モデルとの精度比較
Haruki Okuyama
October 27, 2019
Tweet
Share
More Decks by Haruki Okuyama
See All by Haruki Okuyama
Prophetを使ったコスパの良い時系列データ予測 / prophet-use-cases
spring1018
0
110
Other Decks in Business
See All in Business
RDRAで価値を可視化する
kanzaki
2
350
【SRE Kaigi 2026】認知負荷を最小化するオブザーバビリティとSLOの導入 ―4名SREが200名のコードエンジニアを支援
higuchi_takashi
2
940
採用ピッチ資料
s_kamada
0
280
採用サイト 中途ページ添付資料
naomichinishihama
0
270
【Progmat】ST-Market-Outlook-2026
progmat
0
900
スタートアップ調査:女性起業家を取り巻く課題と解決策
mpower_partners
PRO
0
530
株式会社TENET 会社紹介資料
tenetinc
1
22k
動画編集スクールブイプロ_ファクトブック2026
stakayama
0
430
(15枚)NotebookLMのスライド生成機能で「絶対達成」「予材管理」「大量行動」の重要性を解説してもらう
nyattx
PRO
0
160
Sreake事業部説明資料
3shake
0
360
MEEM_Company_Deck202512.pdf
info_meem
0
3.5k
Growth Book
kuradashi
0
2.9k
Featured
See All Featured
Tips & Tricks on How to Get Your First Job In Tech
honzajavorek
0
430
Evolving SEO for Evolving Search Engines
ryanjones
0
120
GitHub's CSS Performance
jonrohan
1032
470k
The Myth of the Modular Monolith - Day 2 Keynote - Rails World 2024
eileencodes
26
3.3k
Test your architecture with Archunit
thirion
1
2.1k
Site-Speed That Sticks
csswizardry
13
1.1k
Making Projects Easy
brettharned
120
6.6k
Conquering PDFs: document understanding beyond plain text
inesmontani
PRO
4
2.3k
Joys of Absence: A Defence of Solitary Play
codingconduct
1
290
Git: the NoSQL Database
bkeepers
PRO
432
66k
Navigating Algorithm Shifts & AI Overviews - #SMXNext
aleyda
0
1.1k
Chasing Engaging Ingredients in Design
codingconduct
0
110
Transcript
©2019 Wantedly, Inc. ProphetΛͬͨ࣌ܥྻσʔλ༧ଌͱ ػցֶशϞσϧͱͷൺֱ ML for Beginners! MeetUp #1
LTձ Oct 27, 2019 - Haruki Okuyama - @spring1018x
©2019 Wantedly, Inc. Self-Introduction •Haruki OkuyamaʢԞࢁ ݰكʣ •Chemistry Research (until
March 2019) •Wantedly, Inc. (since April 2019) •Recommendation Team • Mainly, Data Analysis
©2019 Wantedly, Inc. ͢͜ͱ ɾ౷ܭϞσϧͱػցֶशϞσϧͷ࣌ܥྻσʔλ༧ଌ ɾ࣌ܥྻ༧ଌϥΠϒϥϦProphetͷհ ͞ͳ͍͜ͱ ɾProphetͷৄࡉͳΞϧΰϦζϜ About this
talk
©2019 Wantedly, Inc. ɾֶशσʔλ(աڈ)ͱςετσʔλ(ະདྷ)ͷ͕ҟͳΔ ɾτϨϯυ͕͋Δͱ2ͭͷ͕ҟͳΔͷવ ɾػցֶशϞσϧΑΓ౷ܭϞσϧͷํ͕༧ଌਫ਼͕͍͍߹͋ͬͨΓ ͢ΔΒ͍͠* => ࣌ܥྻσʔλʹରͯ͠ػցֶशϞσϧͱ౷ܭϞσϧͷ༧ଌ݁ՌΛ ൺֱ͠,
ײ৮Λ͔֬Ί͍ͨʂ ࣌ܥྻσʔλͷ༧ଌͷ͠͞ *https://tjo.hatenablog.com/entry/2019/09/18/190000 https://t.co/S3BpRgtxUW?amp=1
©2019 Wantedly, Inc. ɾػցֶशϞσϧ ϥάಛྔΛத৺ʹ15ݸͷಛྔΛ࡞͠, LightGBMͰֶश ɾ౷ܭϞσϧ FacebookOSSͷProphetΛ༻ : ͋Δࢦඪ͕དྷ݄͍ͭ͘ʹͳΔ͔Λ༧ଌ͢Δ
* ͋Δͷ࣌Ͱ࣍ͷ݄ͷࢦඪΛ༧ଌ͍ͨ͠ ** 3࣌ؒ͘Β͍Ͱ༧ଌϞσϧΛ࡞͍ͨ͠
©2019 Wantedly, Inc. Prophetͬͯʁ 'BDFCPPLͷ044ඇઢܗͳ࣌ܥྻσʔλΛقઅੑٳޮՌΛऔΓೖΕͯ༧ଌ͢Δ ɾτϨϯυ g(t) ɾقઅੑ(पظੑ) s(t) ɾٳɾॕޮՌ
h(t) ɾΠϕϯτΩϟϯϖʔϯ Forecasting at Scale Sean J. Taylor∗† Facebook, Menlo Park, California, United States ࣌ܥྻσʔλΛ͜ΕΒͷཁૉͷͱߟ͑Δ (*ࣗݾ૬ؔߟྀ͍ͯ͠ͳ͍)
©2019 Wantedly, Inc. model࡞ Time 2016-01-01 ~ 2019-06-30·ͰͷσʔλΛֶͬͯश͠, modelΛ࡞ 2019-07-01
~ 2019-09-30ͰධՁ
©2019 Wantedly, Inc. modelධՁ Prophetͷํ͕ਫ਼͕ߴ͍ 2016-01-01 ~ 2019-06-30·ͰͷσʔλΛֶͬͯश͠, modelΛ࡞ 2019-07-01
~ 2019-09-30ͰධՁ Prophet ML MAPE: 12.2% MAPE: 10.6%
©2019 Wantedly, Inc. ͜͜·Ͱͷ·ͱΊ ɾτϨϯυɾपظੑͷڧ͍࣌ܥྻσʔλʹରͯ͠, ػցֶशϞσϧͷ߹, ࣌ؒͰਫ਼Λग़͢ͷ͍͠ ɾͬͱ࣌ؒΛ͔͚ΒΕΔ߹ಛྔઃܭɾόϦσʔγϣϯͷͷ༨ ͕͋ΔͷͰ࣌ܥྻϞσϧʹউͯͦ͏
©2019 Wantedly, Inc. Prophet: ֤ཁૉͷӨڹ ɾτϨϯυ g(t) ɾقઅੑ s(t) ɾٳɾॕޮՌ
h(t) ɾΠϕϯτΩϟϯϖʔϯ ɾٳॕ͚ͩͰͳ͘, ҙͷΠϕϯτΩϟϯϖʔϯͷޮՌऔΓೖΕΔ͜ͱ͕Ͱ͖Δ ɾ͞Βʹ, ֤ཁૉͷӨڹఆྔతʹࢉग़Ͱ͖Δ
©2019 Wantedly, Inc. Prophet: ֤ཁૉͷӨڹ τϨϯυ, ༵ɾ݄ͷӨڹ, ॕͷӨڹͷՄࢹԽ ॱௐʹ Լ
3্݄ঢ trend holiday weekly yearly ॕԼ
©2019 Wantedly, Inc. Prophetͷಛ ɾτϨϯυɾपظੑͷڧ͍࣌ܥྻσʔλʹ͍͍ͯΔ ɾ֤ͷӨڹ͕ఆྔతʹѲͰ͖ΔͷͰઆ໌ੑ͕ߴ͍ ɾύϥϝʔλνϡʔχϯάͦΕ΄Ͳඞཁͳ͍ ɾతͱ͢ΔࢦඪͷυϝΠϯࣝ͑͋͞ΕΑ͍. ྫ༵͑पظ͕͋Δ, ࿈ٳԼ͢Δetc
©2019 Wantedly, Inc. ɾ࣌ܥྻσʔλ༧ଌʹ͓͚ΔػցֶशϞσϧͱProphetͷൺֱ ɾઃఆ͔͚ΒΕΔ࣌ؒΛߟྀͯ͠બ͢Δ ɾProphetʹΑΔ༧ଌ ɾखܰʹ࣌ܥྻ༧ଌ͕Մೳ ɾυϝΠϯ͕ࣝ͋ΕपظɾΠϕϯτޮՌͷઃఆʹΑΓਫ਼্͕͕Δ ɾσʔλΛߏ͢ΔཁૉͷӨڹ͕ఆྔతʹΘ͔Δ Summary