Kediska Saadaasha Dalabka AI

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Why can one global forecasting model help when an organization has many related series?
   • It can learn shared patterns across series while retaining series-specific inputs
   • It guarantees the same forecast error for every series
   • It replaces chronological validation with a random split
   • It means one model is trained separately for each series
2. Maxay saadaasha suurtogalka ah (tiro leh) ugu qiimo badan tahay qorsheynta agabka?
   • Waxay bixiyaan hal lambar oo dammaanad ah
   • Waxay muujinayaan kala duwanaansho iyo kalsooni si ay qorshayayaashu u dejiyaan kayd badbaado oo liddi ku ah khatarta
   • Waxay meesha ka saarayaan baahida loo qabo xog kasta oo taariikhi ah
   • Waxay saadaasha ka dhigaan si degdeg ah
3. Waa kuwee habka saadaalinta tirakoobka caadiga ah ee ka horreeyay hababka casriga ah ee ML?
   • Transformer Fusion ku meel gaadh ah
   • DeepAR
   • ARIMA
   • TimesFM
4. Waa maxay godka muhiimka ah ee la iska ilaalinayo marka la tababarayo saadaaliyaha baahida?
   • Isticmaalka sifooyinka jadwalka
   • Oo ay ku jiraan macluumaadka xayaysiinta
   • Soo saarista kala-soocidda muddada
   • U daadinta xogta mustaqbalka ee tababarka saadaasha hore
5. Calaamadee dibadda ah ayaa si macquul ah u hagaajin doonta saadaasha iibka jalaatada?
   • Heerkulka maalinlaha ah iyo cimilada
   • Diiwaanada wakhtiga wakhti xaadirka ee Server-ka
   • Dhalashadii agaasimaha guud
   • Xulashada xarfaha ee shabakada
6. What did the cited decoder-only foundation-model paper report about its model’s zero-shot forecasts?
   • It guarantees better forecasts for every business series
   • On several public datasets, out-of-the-box accuracy came close to supervised models trained for each dataset
   • It eliminates the need for statistical baselines
   • It forecasts only with fine-tuning on each target series
7. What architecture does the DeepAR paper use to produce forecasts across related time series?
   • A single ARIMA model fitted separately to each series
   • A static lookup table of future values
   • An autoregressive recurrent network trained on many related series
   • A diffusion image model that reads plotted charts
8. Why evaluate a demand model against statistical baselines?
   • To ensure a neural model always wins
   • To make data leakage unavoidable
   • To remove uncertainty from demand
   • To test whether added model complexity improves the target forecast task