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Prophetを使った時系列予測

 Prophetを使った時系列予測

- Prophetアルゴリズムの基本
- 検証のやり方
- 季節性での加法と乗法の使い分け
- 変数重要度と変数ごとの効果

Kan Nishida
PRO

February 26, 2020
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  1. Exploratory ηϛφʔ #25
    ProphetΛ࢖ͬͨ࣌ܥྻ༧ଌ

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  2. EXPLORATORY

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  3. 3
    εϐʔΧʔ
    ੢ా צҰ࿠
    CEO
    EXPLORATORY
    ུྺ
    2016೥य़ɺσʔλαΠΤϯεͷຽओԽͷͨΊɺExploratory, Inc Λཱ
    ্ͪ͛Δɻ
    Exploratory, Inc.ͰCEOΛ຿ΊΔ͔ͨΘΒɺσʔλαΠΤϯεɾϒʔ
    τΩϟϯϓɾτϨʔχϯάͳͲΛ௨ͯ͠σʔλαΠΤϯεͷٕज़ͱख
    ๏ͷීٴͱڭҭʹऔΓ૊Ήɻ
    ถΦϥΫϧຊࣾͰɺ16೥ʹΘͨΓσʔλαΠΤϯεͷ։ൃνʔϜΛ཰
    ͍ɺػցֶशɺϏοάɾσʔλɺϏδωεɾΠϯςϦδΣϯεɺσʔ
    λϕʔεʹؔ͢Δ਺ଟ͘ͷ੡඼ΛੈʹૹΓग़ͨ͠ɻ
    @KanAugust

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  4. Vision
    ΑΓΑ͍ҙࢥܾఆΛ͢ΔͨΊʹ
    σʔλΛ࢖͏͜ͱ͕౰ͨΓલʹͳΔ

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  5. Mission
    σʔλαΠΤϯεͷຽओԽ

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  6. 6
    ୈ̏ͷ೾
    σʔλαΠΤϯεɺAIɺػցֶश͸౷ܭֶऀɺ։ൃऀͷͨΊ͚ͩͷ΋ͷͰ͸͋Γ·ͤΜɻ
    σʔλʹڵຯͷ͋ΔਓͳΒ୭΋͕ੈքͰ࠷ઌ୺ͷΞϧΰϦζϜΛ࢖ͬͯ
    ϏδωεσʔλΛ؆୯ʹ෼ੳͰ͖Δ΂͖Ͱ͢ɻ
    Exploratory͕ͦ͏ͨ͠ੈքΛՄೳʹ͠·͢ɻ

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  7. ୈ1ͷ೾ ୈ̎ͷ೾ ୈ̏ͷ೾
    ϓϥΠϕʔτ(ߴ͍/ݹ͍) Φʔϓϯɾιʔε(ແྉ/࠷ઌ୺)
    UI & ϓϩάϥϛϯά ϓϩάϥϛϯά
    2016
    2000
    1976
    ϚωλΠθʔγϣϯ ίϞσΟςΟԽ ຽओԽ
    ౷ܭֶऀ σʔλαΠΤϯςΟετ
    Exploratory
    ΞϧΰϦζϜ
    Ϣʔβʔɾ
    ମݧ
    πʔϧ
    Φʔϓϯɾιʔε(ແྉ/࠷ઌ୺)
    UI & ࣗಈԽ
    ϏδωεɾϢʔβʔ
    ςʔϚ
    σʔλαΠΤϯεͷຽओԽ

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  8. 質問
    ExploratoryɹϞμϯˍγϯϓϧ UI
    伝える
    データアクセス
    データ
    ラングリング
    可視化
    アナリティクス
    統計/機械学習

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  9. Exploratory ηϛφʔ #25
    ProphetΛ࢖ͬͨ࣌ܥྻ༧ଌ

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  10. ࣌ܥྻ༧ଌϞσϧ
    • ڭࢣ͋ΓֶशͷҰछɻ
    • աڈͷ࣌ܥྻσʔλΛτϨʔχϯάσʔλͱͯ͠ɺকདྷͷ஋Λ༧ଌ
    ͢ΔϞσϧΛ࡞Δɻ
    10

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  11. 11
    աڈN೔ͷσʔλ͔Βɺ࣍ͷ೔ͷ஋Λ༧ଌ͢ΔϞσϧΛ࡞੒͢Δɻ
    ࣌ܥྻ༧ଌ - ఻౷తͳΞϓϩʔν
    Day 1 Day 2 Day 3 Day 4
    Day 2 Day 3 Day 4 Day 5
    Day 3 Day 4 Day 5 Day 6

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  12. 12
    • σʔλؒͷִ࣌ؒؒ͸σʔλΛ௨ͯ͠ҰఆͰ͋Δඞཁ͕͋Δɻ
    • ஋͕NA (ܽଛ஋)ͱͳΔ೔͕͋ͬͯ͸ͳΒͳ͍ɻ
    • ෳ਺ͷपظੑ (िͱ೥) Λಉ࣌ʹѻ͏ͷ͸೉͍͠ɻ
    • ύϥϝʔλͷઃఆʹɺઐ໳తͳ஌͕ࣝඞཁɻ
    ఻౷తͳΞϓϩʔνͰͷ࣌ܥྻ༧ଌͷ໰୊

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  13. 13
    • Facebookʹ͍ͨσʔλαΠΤϯςΟετ
    ʢSean J. Taylor & co.ʣ͕࡞ͬͨ࣌ܥྻ༧
    ଌΞϧΰϦζϜͰɺΦʔϓϯιʔεͱͯ͠
    ެ։͞Ε͍ͯΔɻ(https://
    facebook.github.io/prophet)
    • ౷ܭɺ࣌ܥྻ༧ଌͷઐ໳஌͕ࣝͳͯ͘΋
    ࢖͑ΔΑ͏ʹσβΠϯ͞Ε͍ͯΔɻ
    Prophet
    Sean J. Taylor
    @seanjtaylor

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  14. 14
    • ҎԼͷཁૉͷ࿨ͱͯ͠ද͢͜ͱͷͰ͖Δ׈Β͔ͳۂઢͷ͏ͪɺաڈ
    σʔλʹ࠷΋ϑΟοτ͢Δ΋ͷΛ୳͢ɻͦͷۂઢΛະདྷʹԆ௕͢Δ
    ͜ͱʹΑͬͯ༧ଌ͢Δɻ
    • େہతͳ੒௕τϨϯυ
    • पظͷقઅੑ (೥ɺिɺ೔ͳͲ)
    • ॕ೔ޮՌ - ΫϦεϚεɺ৽೥ɺ೥࣍ΠϕϯτɺͳͲɻ
    • ֎෦༧ଌม਺
    Prophet

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  15. 15
    େہతͳτϨϯυ

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  16. 16
    େہతͳτϨϯυ

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  17. 17
    ೥पظͷقઅੑ

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  18. 18
    τϨϯυ + ೥पظੑ

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  19. 19
    िपظͷมಈ

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  20. 20
    τϨϯυ + ೥पظੑ + िपظੑ

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  21. 21
    τϨϯυ + ೥पظੑ + िपظੑ
    21

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  22. 22
    ॕ೔ޮՌ

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  23. 23
    τϨϯυ + ೥पظੑ + िपظੑ + ॕ೔ޮՌ

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  24. 24
    • ࣌ؒͷൃలΛϞσϧͰදݱ͢Δ͜ͱ͸͖͋ΒΊΔɻ
    • ͔ΘΓʹɺ୯ʹۂઢΛݟ͚ͭΔͱ͍͏໰୊ʹ͢Δ͜ͱʹΑͬͯҎԼͷΑ͏ͳ
    ར఺Λಘ͍ͯΔɻ
    • σʔλؒͷִ͕࣌ؒؒҰఆͰ͋Δඞཁ͸ͳ͍ɻ
    • ஋͕NA (ܽଛ஋)ͱͳΔ೔͕͋ͬͯ΋໰୊ͳ͍ɻ
    • ෳ਺ͷपظੑ (िͱ೥) ͕σϑΥϧτͰߟྀ͞ΕΔɻ
    • σϑΥϧτͷઃఆͰͦΕͳΓͷ༧ଌ͕Ͱ͖ΔɻઃఆՄೳͳύϥϝʔλͷଟ
    ͘͸ઐ໳஌ࣝແ͠ͰཧղՄೳɻ
    Prophetͷར఺

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  25. Let’s do it!
    25

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  26. Global Sales σʔλ
    26

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  27. 27
    Sales (ച্)ΛϥΠϯνϟʔτͰՄࢹԽͯ͠ΈΔ
    • X࣠ʹOrder DateΛɺ࣌ؒ୯ҐΛ
    WEEKͱׂͯ͠Γ౰ͯΔɻ
    • Y࣠ʹSalesΛɺؔ਺ΛSUM(߹
    ܭ)ͱׂͯ͠Γ౰ͯΔɻ

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  28. 28

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  29. Salesʢച্ʣΛ༧ଌ͢ΔϞσϧΛ࡞Δ
    29

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  30. ΞφϦςΟΫεɾϏϡʔΛબ୒͠ɺ࣌ܥྻ༧ଌΛબ୒ɻ
    30

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  31. • ೔෇/࣌ؒͷྻʹOrder DateΛ࣌
    ؒ୯ҐΛWEEKͱׂͯ͠Γ౰ͯ
    Δɻ
    • ஋ͷྻʹSalesΛɺؔ਺Λ
    SUM(߹ܭ)ͱׂͯ͠Γ౰ͯΔɻ
    31

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  32. ϓϩύςΟɾμΠΞϩάΛ࢖ͬͯɺ
    1೥ؒͷ༧ଌΛ͢ΔͨΊʹɺ༧ଌظ
    ؒΛ52 (1೥ʹ52ि) ʹઃఆ͢Δɻ
    32

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  33. ࣮ߦϘλϯΛΫϦοΫ͢Δͱ༧ଌ͞Εͨσʔλ͕ΦϨϯδ৭ͷϥΠϯͰදࣔ͞ΕΔɻ
    33
    ΦϨϯδ৭ͷઢ͚ͩͷ۠ؒ͸ࠓޙ1೥ؒͷ༧ଌΛද͢ɻ

    ୶͍ΦϨϯδ৭͸༧ଌͷෆ֬ఆ۠ؒ(uncertainty interval)Λද͢ɻ

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  34. 34
    τϨϯυ λϒΛΫϦοΫ͢ΔͱτϨϯυϥΠϯͷೖͬͨνϟʔτ͕දࣔ͞ΕΔ
    άϦʔϯͷઢ͕τϨϯυϥΠϯ

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  35. 35
    ྘ͷόʔ͸ɺτϨϯυʹมԽ͕͋ͬͨ࣌఺ʢνΣϯδϙΠϯτʣΛද͢ɻ
    ๮ͷߴ͞͸ɺνΣϯδϙΠϯτͰͷτϨϯυϥΠϯͷ܏͖ͷมԽྔɻ

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  36. 36
    ೥पظλϒΛΫϦοΫ͢Δͱ೥पظͷνϟʔτ͕දࣔ͞ΕΔɻ

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  37. Every year, the sales doesn’t pick up until June, then it goes down in July.

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  38. Weekly tab shows up only when the data is daily or more granular levels (hour, minutes, etc.)

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  39. Every week, the sales are low on Sunday and Monday,
    and the rest of the week is high.

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  40. 40
    िपظΛݟΔͨΊʹɺ࣌ؒ୯ҐΛ೔ʹมߋ࣮ͯ͠ߦ͠௚͢ɻ

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  41. σʔλͷલॲཧ
    ܽଛ஋ʢNAʣͷॲཧ

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  42. Somehow, the weekly seasonality doesn’t repeat exactly the same…

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  43. Somehow, the weekly seasonality doesn’t repeat exactly the same…

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  44. There are NA for some dates. You can impute NA as part of the Data Preprocessing.

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  45. 45
    • ஫จ͕Ұ݅΋ͳ͍೔ͷച্Λ0ͱͯ͠ѻ͏ͨ
    Ίɺ஋ͷྻʹ஋͕ͳ͍ͱ͖ͷॲཧʹ”θϩͰ
    ຒΊΔ”ΛબͿɻ
    • ͜ΕΛ͠ͳ͍৔߹ɺ஫จ͕Ұ݅΋ͳ͍೔
    ͸ɺσʔλ͕ແ͍೔ͱͯ͠ѻΘΕΔɻ

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  46. View Slide

  47. View Slide

  48. Under the Importance tab, you can see which seasonality has more effect
    on the forecasting outcome.

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  49. 49
    िपظλϒΛΫϦοΫ͢Δͱिपظͷνϟʔτ͕දࣔ͞ΕΔɻ

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  50. 50
    ޮՌλϒΛΫϦοΫ͢Δͱɺ༧ଌ஋Λߏ੒͢Δ֤ཁૉʢτϨϯυɺقઅੑʣ͕දࣔ͞ΕΔɻ

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  51. 51
    ม਺ॏཁ౓λϒΛΫϦοΫ͢ΔͱԿ͕༧ଌʹେ͖ͳӨڹΛ༩͍͑ͯΔͷ͔͕ݟ͑Δɻ

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  52. 52
    σʔλλϒΛΫϦοΫ͢Δͱ༧ଌ෇͖ͷσʔλ͕දࣔ͞ΕΔ

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  53. • forecasted_value - ༧ଌ஋
    • forecasted_value_high/forecasted_value_low - ෆ֬ఆ۠ؒ
    • trend - େہతͳ੒௕τϨϯυ
    • yearly - ೥पظͷτϨϯυ
    • weekly - िपظͷτϨϯυ
    53
    ༧ଌ෇͖ͷσʔλͷಡΈํ

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  54. 54
    ࣌ܥྻ༧ଌͷධՁ

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  55. 55
    όοΫςετ
    • աڈσʔλͷ͏ͪɺ৽͍͠ظؒΛςετ༻ʹͱ͓ͬͯ͘ɻ
    • ςετظؒͷσʔλΛɺͦΕΑΓҎલͷσʔλΛ࢖ͬͯ༧ଌ͢Δ
    • ༧ଌσʔλͱɺ࣮ࡍͷςετظؒͷσʔλΛൺֱධՁ͢Δɻ
    ࣌ܥྻ༧ଌͷςετ๏

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  56. σʔλΛ2ͭͷηΫγϣϯʹ෼͚Δɻ
    56
    τϨʔχϯάظؒ ςετظؒ

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  57. 57
    τϨʔχϯάσʔλΛ࢖ͬͯ༧ଌϞσϧΛ࡞ΓɺςετظؒΛ༧ଌɻ
    ςετظؒͷ࣮ଌ஋ͱ༧ଌ஋ͷʮͣΕʯͷେ͖͞ΛධՁ͢Δɻ
    τϨʔχϯάظؒ ςετظؒ

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  58. 58
    • ࣌ؒͷ୯ҐʹMON(݄)Λࢦఆ͢Δɻ
    ςετϞʔυʹ੾Γସ͑Δ

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  59. 59
    ςετϞʔυΛTRUEʹ͠ɺςετظؒΛ12 (݄) ͱ͢Δɻ

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  60. ࠨଆͷ੨͍ઢ͸τϨʔχϯάσʔλɺӈଆͷਫ৭ͷઢ͸ςετ
    σʔλɻ
    60

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  61. 61
    ੨৭ͷઢ͕τϨʔχϯάσʔλɻ
    ͜ͷൣғσʔλΛ΋ͱʹɺ༧ଌϞσϧ͕࡞ΒΕΔɻ

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  62. ΦϨϯδͷઢ͕ɺτϨʔχϯάσʔλΛݩʹ࡞ΒΕͨϞσϧʹ
    ΑΔ༧ଌσʔλɺബ͍ΦϨϯδͷྖҬ͸ͦͷ৴པ۠ؒͰ͋Δɻ
    62

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  63. ͜ͷൣғͷσʔλΛ΋ͱʹ༧ଌϞσϧ͕࡞ΒΕ͍ͯΔͨΊɺ

    ͜ͷൣғͷ࣮σʔλͱϞσϧʹΑΔ༧ଌσʔλ͸͔ͳΓҰக͍ͯ͠Δɻ
    63

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  64. ςετظؒͷ༧ଌσʔλͱ࣮ଌ஋ͷζϨ͕ͲΕ͚ͩେ͖͍ͷ͔Λ
    ࢉग़͠ɺͦΕΛ࣋ͬͯϞσϧͷਫ਼౓ΛධՁ͍ͨ͠ɻ
    64

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  65. ςετ݁ՌͷαϚϦ
    65

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  66. 66
    • RMSE (Root Mean Square Error) : ༧ଌ͔ΒͷͣΕͷೋ৐ͷฏۉͷϧʔτ
    • MAE (Mean Absolute Error) : ༧ଌ͔ΒͷͣΕͷઈର஋ͷฏۉ
    • MAPE (Mean Absolute Percentage Error) : ύʔηϯτͰදͨ͠༧ଌ͔Βͷ
    ͣΕͷઈର஋ͷฏۉ
    • MASE (Mean Absolute Scaled Error) : MAEΛɺτϨʔχϯάσʔλͰͷφ
    Πʔϒ༧ଌʢҰͭલͷظͱಉ͡஋͕ݱΕΔͰ͋Ζ͏ͱ͍͏୯७ͳ༧ଌʣ
    ͷMAEͰׂͬͨ΋ͷɻ
    ࣌ܥྻ༧ଌͷධՁࢦඪ

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  67. Rootʢฏํࠜʣ

    Meanʢฏۉʣ

    Squareʢ2৐ʣ

    Errorʢޡࠩʣ
    ͭ·Γɺ࣮ଌ஋ͱ༧ଌ஋ͷޡࠩ
    Λ2৐ͯ͠ɺͦͷฏۉΛͱΓɺͦ
    ͷ஋ͷฏํࠜΛͱͬͨ஋ͷ͜
    ͱɻ
    67
    RMSE (Root Mean Square Error)

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  68. 22 + 22 + 22 + 42 

    4 (఺ͷ਺)
    4 + 4 + 4 + 16 

    4
    7 = 2.65
    68
    RMSE (Root Mean Square Error)
    2 2 4
    2
    =
    =
    ྫ͑͹ɺ࣮ଌ஋ͱ༧ଌ஋ͷޡ͕ࠩͦΕ
    ͧΕ2, 2, 2, 4ͩͬͨͱ͢Δͱɺܭࢉ͸
    ҎԼͷΑ͏ʹͳΔɻ

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  69. Meanʢฏۉʣ

    Absoluteʢઈର஋ʣ

    Errorʢޡࠩʣ
    ͭ·Γɺ࣮ଌ஋ͱ༧ଌ஋ͷޡࠩ
    ͷઈର஋ͷฏۉ͜ͱɻ
    69
    MAE (Mean Absolute Error)

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  70. 2 + 2 + 2 + 4 

    4 (఺ͷ਺)
    70
    ྫ͑͹ɺ࣮ଌ஋ͱ༧ଌ஋ͷޡ͕ࠩ
    ͦΕͧΕ2, 2, 2, 4ͩͬͨͱ͢Δ
    ͱɺܭࢉ͸ҎԼͷΑ͏ʹͳΔɻ
    = 2.5
    MAE (Mean Absolute Error)
    2 2 4
    2

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  71. Meanʢฏۉʣ

    Absoluteʢઈର஋ʣ

    Percentageʢׂ߹ʣ

    Errorʢޡࠩʣ
    ͭ·Γɺ࣮ଌ஋ͱ༧ଌ஋ͷޡࠩ
    ͷׂ߹ͷઈର஋ͷฏۉ͜ͱɻ
    71
    MAPE (Mean Absolute Percentage Error)

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  72. 72
    12 13 16
    11
    ·ͣɺ࣮ଌ஋Λ΋ͱΊΔɻ
    MAPE (Mean Absolute Percentage Error)

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  73. 73
    12 13 16
    11
    MAPE (Mean Absolute Percentage Error)
    2 2
    4
    2
    ࣍ʹɺ࣮ଌ஋ͱ༧ଌ஋ͷޡࠩ
    Λ΋ͱΊΔɻ

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  74. 74
    100 100 100
    100
    MAPE (Mean Absolute Percentage Error)
    16.6% 15.4%
    25%
    18.2%
    ࣮ଌ஋ͱ༧ଌ஋ͷޡࠩΛ࣮ଌ஋Ͱ
    ׂͬͯ100Λ͔͚ɺͦΕͧΕͷ
    ύʔηϯςʔδΛ΋ͱΊΔɻ
    ਺ࣈ͕ϚΠφεͷ৔߹ɺϚΠφε
    ͷූ߸ΛͱΔ (ઈର஋).

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  75. 75
    100 100 100
    100
    MAPE (Mean Absolute Percentage Error)
    16.6% 15.4%
    25%
    18.2%

    16.6 + 15.4 + 18.2 + 25 

    4 (఺ͷ਺)
    ࠷ޙʹɺ͜ΕΒͷ஋ͷฏۉΛग़͢ɻ
    = 18.8%

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  76. 76
    Meanʢฏۉʣ

    Absoluteʢઈର஋ʣ

    Scaledʢεέʔϧௐ੔ࡁΈͷʣ

    Errorʢޡࠩʣ
    MAEΛҟͳΔεέʔϧͷσʔλ
    Ͱͷ༧ଌͲ͏͠Ͱ΋ൺֱՄೳͳ
    Α͏ʹεέʔϧௐ੔ͨ͠΋ͷɻ
    MASE (Mean Absolute Scaled Error)

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  77. 77
    MASE = ςετظؒͷMAE / τϨʔχϯάظؒͷφΠʔϒ༧ଌͷMAE
    MASE
    ςετظؒͷMAE
    τϨʔχϯάظؒͷφΠʔϒ༧ଌͷMAE

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  78. 78
    Ұظલͷ஋͕ɺࠓظ΋ͦͷ··ग़ΔͩΖ͏ɺͱ͍͏҆қͳ༧ଌɻ
    φΠʔϒ༧ଌ

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  79. 79
    φΠʔϒ༧ଌΛτϨʔχϯάظؒʹରͯ͠ߦͬͨͱ͖ͷMAEΛج४ͱͯ͠࠾༻
    MASEͷεέʔϧௐ੔ͷج४

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  80. 80
    MASE = ςετظؒͷMAE / τϨʔχϯάظؒͷφΠʔϒ༧ଌͷMAE
    MASE
    ςετظؒͷMAE
    τϨʔχϯάظؒͷφΠʔϒ༧ଌͷMAE

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  81. 81
    قઅੑͷϞʔυ
    Ճ๏త vs ৐๏త

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  82. 82
    The difference between the actual line and the forecasted line
    becomes wider as the time progresses.

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  83. Կ͕ى͖͍ͯΔͷ͔ʁ
    83
    • ച্͕੒௕͢ΔʹͭΕɺͦΕʹ͋ΘͤͯقઅੑʹΑΔมಈ΋େ͖͘ͳΔͱ
    ߟ͑Δͷ͕ࣗવɻ
    • ͔͠͠Ϟσϧ͸قઅੑʹΑΔมಈͷେ͖͞͸͍ͭͰ΋ҰఆͰ͋Δͱ͍͏લ
    ఏͰ༧ଌ͍ͯ͠Δɻ
    • ͕࣌ؒͨͬͯച্͕੒௕ͨ͋͠ͱͰ͸ɺϞσϧ͕༧ଌ͢Δقઅมಈͷେ͖
    ͕͞ɺ࣮ࡍͷ஋ͷقઅมಈͷେ͖͞ʹ͍͍͚͍ͭͯͯͳ͍ͷͰ͸ͳ͍͔ʁ

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  84. ࿨ͱͯ͠੒Γཱ͍ͬͯΔ஋ͷϦχΞͳ৳ͼํ ੵͱͯ͠੒Γཱ͍ͬͯΔ஋ͷෳརޮՌͷ͋Δ৳ͼํ
    མͪண͍ͯΔձࣾͷैۀһ਺ͷਪҠ Amazonͷച্ߴͷਪҠ
    ੒௕͢Δͱ͖ͷ஋ͷ৳ͼํͷҧ͍
    84

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  85. ઌఔͷςετϞʔυͰͷ༧ଌ݁ՌΛ΋͏Ұ౓ݟͯΈΔͱ…
    85
    ࣮ࡍͷقઅมಈ͸ɺ༧ଌ݁ՌΑΓ΋
    େ͖͘ͳ͍ͬͯΔΑ͏ͩɻ

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  86. Կ͕ى͖͍ͯΔͷ͔ʁ
    86
    ച্ͷ੒௕ʹͱ΋ͳͬͯେ͖͘ͳΔقઅมಈΛϞσϧԽͰ͖Ε͹Α͍ͷͰ͸ʁ
    • ച্͕੒௕͢ΔʹͭΕɺͦΕʹ͋ΘͤͯقઅੑʹΑΔมಈ΋େ͖͘ͳΔͱ
    ߟ͑Δͷ͕ࣗવɻ
    • ͔͠͠Ϟσϧ͸قઅੑʹΑΔมಈͷେ͖͞͸͍ͭͰ΋ҰఆͰ͋Δͱ͍͏લ
    ఏͰ༧ଌ͍ͯ͠Δɻ
    • ͕࣌ؒͨͬͯച্͕੒௕ͨ͋͠ͱͰ͸ɺϞσϧ͕༧ଌ͢Δقઅมಈͷେ͖
    ͕͞ɺ࣮ࡍͷ஋ͷقઅมಈͷେ͖͞ʹ͍͍͚͍ͭͯͯͳ͍ͷͰ͸ͳ͍͔ʁ

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  87. قઅੑϞʔυ
    87
    Ճ๏త
    ଍͠ࢉͰޮՌ͕ݱΕΔɻ

    ྫɿ12݄͸ϓϥε$100,000

    ΋ͱͷ஋ʹؔΘΒͣมಈͷେ͖͞͸Ұ
    ఆɻ
    ৐๏త
    ֻ͚ࢉͰޮՌ͕ݱΕΔ

    ྫɿ12݄͸ϓϥε10%

    ΋ͱͷ஋͕େ͖͚Ε͹มಈ΋େ͖
    ͘ͳΔɻ

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  88. 88
    Ճ๏త
    ৐๏త

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  89. 89
    Ճ๏త
    ৐๏త
    قઅੑͷେ͖͞͸Ұఆ

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  90. 90
    Ճ๏త
    ৐๏త
    قઅੑͷେ͖͞͸Ұఆ
    قઅੑ͸ݩͷ஋ʢτϨϯυʣʹൺྫͯ͠େ͖͘ͳΔ

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  91. 91
    Ճ๏త
    ৐๏త
    قઅੑͷେ͖͞͸Ұఆ
    قઅੑ͸ݩͷ஋ʢτϨϯυʣʹൺྫͯ͠େ͖͘ͳΔ

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  92. قઅੑͷϞʔυʢՃ๏తɺ৐๏తʣͷ੾Γସ͑
    92
    ΞφϦςΟΫεɾϓϩύςΟͷقઅੑͷ
    ϞʔυͰɺՃ๏త͔৐๏త͔Λ੾Γ͔͑
    Δ͜ͱ͕Ͱ͖Δɻ

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  93. 93
    Ճ๏త
    ৐๏త

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  94. 94
    Ճ๏త
    ৐๏త
    ৐๏తͳقઅੑΛ࢖ͬͨ༧ଌͷ΄͏
    ͕࣮ଌ஋ʹ༧ଌ஋͕௥ਵ͍ͯ͠Δɻ

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  95. 95
    Ճ๏త
    ৐๏త
    ςετ݁ՌΛΈΔͱɺ৐๏తͳقઅੑΛ࢖ͬͨ༧ଌͷ΄͏͕ɺશͯͷࢦඪ
    ͰɺΑ͍݁Ռʢখ͍͞஋ʣʹͳ͍ͬͯΔɻ

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  96. قઅੑͷϞʔυʢՃ๏తɺ৐๏తʣͷൺֱ
    96
    قઅੑͷύλʔϯ͕ݟ΍͍͢Α͏ʹɺ࣌
    ؒͷ୯ҐΛ͍ͬͨΜWEEK(ि)ʹͯ͠ɺ
    قઅੑͷϞʔυΛՃ๏తͱ৐๏తͷؒͰ
    ੾Γସ͑ͯɺ༧ଌ݁ՌΛൺֱͯ͠ΈΔɻ

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  97. ݄୯Ґʹ໭ͯ͠ɺςετ݁ՌΛൺֱ
    97
    ࣌ؒͷ୯ҐΛݩͷMON(݄)ʹͯ͠ɺقઅ
    ੑͷϞʔυΛՃ๏తͱ৐๏తͷؒͰ੾Γ
    ସ͑ͯɺςετ݁ՌΛൺֱͯ͠ΈΔɻ

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  98. 98
    ʮ܁Γฦ͠ʯΛ࢖ͬͯෳ਺ͷϞσϧΛ࡞Γɺ
    ͦΕΒΛൺֱ͢Δ

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  99. 99
    Ϛʔέοτ͝ͱʹ༧ଌϞσϧΛ࡞Γ͍ͨͷͰɺʮ܁Γฦ͠ʯʹ ’Market’ ྻΛબͿ

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  100. 100

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  101. • RMSE, MAEͰݟΔͱɺAfrica (ΞϑϦΧ)ͷ΄͏͕Asia Pacific (ΞδΞଠฏ༸஍
    Ҭ)ΑΓ༧ଌ஋ͱ࣮ଌ஋ͷ͕ࠩখ͍͜͞ͱ͕෼͔Δɻ
    • ͜ΕΛ΋ͬͯɺAfricaͷํ͕Α͘༧ଌͰ͖͍ͯΔͱݴ͑ΔͩΖ͏͔ʁ
    Ϟσϧͷ༧ଌਫ਼౓ͷࢦඪΛൺ΂Δ
    101

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  102. • Asia Pacific (ΞδΞଠฏ༸஍Ҭ) ͷํ͕Africa (ΞϑϦΧ)ΑΓച্ֹ͕େ͖͍ͷ
    ͰɺRMSE, MAE͕େ͖͘ͳΔͷ͸౰ͨΓલͱݴ͑Δɻ
    • ஋ͷεέʔϧʹ͕ࠩ͋Δͱ͖͸ɺRMSEɺMAEʹΑΔ༧ଌੑೳͷൺֱ͸ҙຯΛ
    ͳ͞ͳ͍ɻ
    Ϟσϧͷ༧ଌੑೳͷαϚϦͷදࣔ
    102

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  103. • MAPE͸ɺ༧ଌ஋ͱ࣮ଌ஋ͷࠩΛɺ࣮ଌ஋ͷύʔηϯτͰදͨ͠΋ͷɻ
    • ࣮ଌ஋ͷ΋ͱ΋ͱͷେ͖͞ʹؔ܎ͳ͘༧ଌੑೳͷൺֱ͕Ͱ͖Δɻ
    • RMSE, MAEͷେ͖͔ͬͨAsia Pacificͷ΄͏͕ɺMAPE͸ΑΓখ͘͞ɺ࣮͸
    AfricaΑΓΑ͍༧ଌ͕Ͱ͖͍ͯͨ͜ͱ͕෼͔Δɻ
    Ϟσϧͷ༧ଌੑೳͷαϚϦͷදࣔ
    103

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  104. • MAPE΋ಉ͡Α͏ʹɺ࣮ଌ஋ͷ΋ͱ΋ͱͷେ͖͞ʹؔ܎ͳ͘༧ଌੑೳͷൺֱ͕Ͱ͖Δɻ
    • MAPE͸ɺ஋͕0Λ·͍ͨͩΓɺ0ʹۙ͘ͳΔͱ͖͸ෆ҆ఆʹͳΔ͕ɺMASEʹ͸͜ͷ໰୊͸ͳ͍ɻ
    • MASEͰݟͯ΋ɺRMSE, MAEͷେ͖͔ͬͨAsia Pacificͷ΄͏͕ɺ࣮͸ΑΓΑ͍༧ଌ͕Ͱ͖͍ͯͨ͜ͱ
    ͕෼͔Δɻ
    Ϟσϧͷ༧ଌੑೳͷαϚϦͷදࣔ
    104

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  105. ֎෦༧ଌม਺

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  106. ച্ʢSalesʣͱ૬ؔؔ܎ʹ͋Δม਺͕͋ͬͨͱͯ͠ɺ͞Βʹͦͷ
    ม਺ͷকདྷͷ஋Λ༧ଌͰ͖Δɺ·ͨ͸ίϯτϩʔϧͰ͖Δͱͨ͠
    Βɺച্ʢSalesʣΛ͞Βʹྑ͍ਫ਼౓Ͱ༧ଌͰ͖ΔͷͰ͸ͳ͍͔ʁ

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  107. Sales ͱ Sales Comp. ͸૬͍ؔͯ͠ΔΑ͏ͩɻ

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  108. Sales ͱ Marketing ΋૬͍ؔͯ͠ΔΑ͏ͩɻ

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  109. Sales ͱ Discount (Avg) ͸͋·Γ૬ؔͯͦ͠͏ʹͳ͍ɻ

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  110. ϚʔέςΟϯά
    ࣍ͷ3ϲ݄ɺͲΕ͚ͩ޿ࠂʹ͓ۚΛඅ΍͔͢ίϯτϩʔϧͰ͖Δͱͨ͠
    Βɺͦͷ৘ใΛϞσϧʹ૊ΈࠐΉ͜ͱ͸Ͱ͖ͳ͍͔ʁ

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  111. ఱؾ
    ച্͸ఱؾʹΑͬͯӨڹΛड͚Δͱ͢Δͱɺ࣍ͷ10೔ؒͷؾԹ΍߱ਫ֬཰
    Λ༧ଌͯ͠ɺͦΕΒΛച্ͷ༧ଌϞσϧʹ૊ΈࠐΉͱɺ͞Βʹྑ͍ਫ਼౓Ͱ
    ച্͛Λ༧ଌͰ͖ΔͷͰ͸ͳ͍ͩΖ͏͔ɻ

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  112. ม਺ʢྻʣΛ֎෦༧ଌม਺ͱׂͯ͠౰ͯɺ༧ଌϞσϧΛ
    ࡞Δ͜ͱ͕Ͱ͖Δɻ
    Prophet͸֎෦༧ଌม਺ʢྫɿϚʔέςΟϯάඅ༻ʣ͕
    λʔήοτม਺ʢྫɿച্ʣΛ༧ଌ͢ΔͨΊʹ໾ཱ͔ͭ
    Λௐ΂ɺͦͷ֎෦༧ଌม਺ͷ܎਺Λ୳͠ग़͢ɻ
    ֎෦༧ଌม਺

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  113. τϨϯυͱ೥पظͷقઅੑΛ΋ͱʹͨ͠༧ଌϞσϧ

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  114. ϕʔεϞσϧͷධՁ

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  115. τϨϯυͱ೥पظͷقઅੑͱച্ใुΛ΋ͱʹͨ͠༧ଌϞσϧ

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  116. ϞσϧͷධՁ
    ϕʔεϞσϧ
    ϕʔεϞσϧʹച্ใुΛ෇͚଍ͨ͠

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  117. τϨϯυͱ೥पظͷقઅੑͱϚʔέςΟϯάඅ༻Λ΋ͱʹͨ͠༧ଌϞσϧ

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  118. ϕʔεϞσϧʹച্ใुΛ෇͚଍ͨ͠
    ϕʔεϞσϧʹϚʔέςΟϯάඅ༻Λ෇͚଍ͨ͠

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  119. ࣍ͷ3ͭશ෦଍ͯ͠ΈΔ
    Sales Comp., Marketing, and Discount

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  120. With Sales Comp., Marketing, Discount

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  121. The forecasting model quality has improved for a little bit.
    ϕʔεϞσϧʹച্ใुΛ෇͚଍ͨ͠
    ϕʔεϞσϧʹച্ใुɺϚʔέςΟϯάඅ༻ɺׂҾ཰Λ෇͚଍ͨ͠

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  122. ޮՌλϒͷԼͰ͸ɺͦΕͧΕͷقઅੑͱ༧ଌม਺͕༧ଌ஋ʹͲͷΑ͏ʹ
    ӨڹΛ༩͑Δͷ͔ΛݟΔ͜ͱ͕Ͱ͖Δɻ

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  123. ม਺ॏཁ౓λϒͷԼͰ͸ɺ࢖ΘΕͨقઅੑͱ༧ଌม਺ͷ͏ͪͲͷม਺͕
    ΑΓӨڹ͕ڧ͍ͷ͔ΛݟΔ͜ͱ͕Ͱ͖Δɻ

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  124. Q & A

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  125. ࣍ճηϛφʔ

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  126. σʔλαΠΤϯε
    X
    3/5 (໦) 1PM (೔ຊ࣌ؒʣ

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  127. View Slide

  128. 5݄։࠵ܾఆʂ
    ฏ೔൛ɿ26೔ʢՐʣɺ27೔ʢਫʣɺ28೔ʢ໦ʣ
    ि຤൛ɿ23೔ʢ౔ʣɺ24೔ʢ೔ʣɺ30೔ʢ౔ʣ

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  129. • ϓϩάϥϛϯάͳ͠
    RݴޠͷUIͰ͋ΔExploratoryΛ෼ੳπʔϧͱͯ͠࢖༻͢ΔͨΊडߨத͸ɺϏδωεͷ
    ໰୊Λղܾ͢ΔͨΊʹඞཁͳσʔλαΠΤϯεͷख๏ͷशಘʹ100ˋूதͰ͖Δ

    • ෼ੳπʔϧͷϕϯμʔϩοΫΠϯͳ͠
    ExploratoryͰͷ࡞ۀ͸શͯಠཱͨ͠ΦʔϓϯιʔεͷR؀ڥͰ࠶ݱ͕Մೳ

    • ϏδωεͰ࢖͑ΔࢥߟྗͱεΩϧͷशಘ
    σʔλαΠΤϯεͷεΩϧशಘ͚ͩͰͳ͘ɺσʔλ෼ੳʹඞཁͳࢥߟྗ΋शಘͰ͖Δ
    ಛ௃

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  130. ࿈བྷઌ
    ϝʔϧ
    [email protected]
    ΢ΣϒαΠτ
    https://ja.exploratory.io
    ϒʔτΩϟϯϓɾτϨʔχϯά
    https://ja.exploratory.io/training-jp
    Twitter
    @KanAugust

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