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Funkcje opóźnień w quizie dotyczącym prognozowania szeregów czasowych

Twórz ważne w czasie funkcje opóźnień i kroczenia, identyfikuj ryzyko wycieków i wybieraj podziały walidacji, które przypominają przyszłe wykorzystanie prognozowania.

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Pytanie 1 z 8

To forecast y_t before time t, which lag feature is temporally valid?

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Odpowiedzi i wyjaśnienia pojawiają się w miarę rozwiązywania quizu.

  1. To forecast y_t before time t, which lag feature is temporally valid?
    • y_(t-1)
    • y_(t+1)
    • The realized value y_t
    • The average of all observations through the test period
  2. Where should a trailing seven-day mean end when predicting the next day's value?
    • At the target day including its actual value
    • At the last fully observed day before the target
    • At the end of the full evaluation dataset
    • At the future forecast horizon
  3. Why can a random split give an unrealistic time-series evaluation?
    • It removes all seasonal structure
    • It makes every lag feature missing
    • Future rows may train a model evaluated on earlier periods
    • It guarantees that test rows precede training rows
  4. When is a planned holiday indicator a valid forecast feature?
    • Whenever the model accuracy improves
    • Only after the holiday has occurred
    • When it is calculated using test outcomes
    • When the calendar information is known at forecast time
  5. Why might a validation split include a gap?
    • To make the forecast horizon random
    • Delayed labels or overlapping windows can otherwise cross the boundary
    • To ensure all features are standardized twice
    • To increase training data with future observations
  6. How should scaling or imputation be fit during time-series cross-validation?
    • Only on the final test set
    • Once on all dates before splitting
    • Within each training fold only
    • Separately on each individual feature after prediction
  7. Which procedure recursively generates a multi-step forecast?
    • Remove all lag features after the first forecast
    • Use only future actual values at every step
    • Train a separate model for each target class
    • Feed earlier forecast predictions into later forecast features
  8. What should a backtest reproduce for reliable assessment?
    • Information available at each forecast origin and the intended retraining cadence
    • Only the final feature matrix after all data are collected
    • A random order of historical targets
    • The model's training accuracy alone