A retailer has historical sales and seasonality by store, product, and date.
It wants to estimate next week's quantity for purchasing.
Which AI use case best fits this requirement?
Estimating a future quantity from observations ordered by time is time-series forecasting.
Detailed explanation
Authentication verifies identity and does not forecast demand.
Historical time-ordered sales can be used to estimate future quantities.
Summarization generates shorter text, not future sales quantities.
Compression reduces storage size and does not estimate future demand.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01ガイドのDomain 1.2で予測タスクと生成タスクの違いを確認する。
Official references
What to confirm
将来の数値を推定する予測と、文章生成・認証・圧縮を区別できる。
Key points
- Time series
- Future values
- Continuous prediction
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Compare the definition with the AIF-C01 objective and the service documentation before applying it.
- Keep the concept separate from a particular answer choice or implementation step.
Foundation review
Temporal order
Training and evaluation must respect time so information from the future does not leak into the past.
Forecast horizon
The horizon states how far beyond the latest observation the system must predict.