Everything else is ready: study the parts, the key terms and the quiz below.
Dependence Over Time
Recognise when nearby observations are related, so an ordinary random split or row bootstrap gives misleading uncertainty.
In this lesson6 parts
- 01A date changes the evaluation question
- 02Detect dependence across days
- 03What a shuffled test measures
- 04Advance the cutoff without training on future dates
- 05Match code and claim to the calendar
- 06Make the forecast claim precise
Key terms
The words this lesson introduces, each in one line. The module’s glossary collects them all.
- daily series
- One measured value for each calendar day.
- time dependence
- The value on a date is associated with values on other dates.
- time series
- Observations indexed in time order.
- lag
- A fixed number of time steps between paired observations.
- autocorrelation
- Association between a series and a lagged copy of itself.
- baseline
- A simple prediction rule used for comparison; here, the training mean for each weekday.
- absolute error
- Distance between an observed count and its prediction, in the same units.
- MAE
- Mean of absolute prediction errors, in trips per day.
- chronological holdout
- A later test period separated from earlier training dates.
- walk-forward evaluation
- Repeating a time-ordered test as the training cutoff advances.
- independent observations
- Observations whose joint variation is not tied by the assumed sampling process.
- temporal leakage
- Use of future information during training or selection for an earlier forecast.
Quiz 5 questions
Your first pick on each question is the one that counts, and a right one earns a coin. Getting one wrong here is how the lesson sticks.
Practice
Problems to solve in your own notebook. Each states the problem, not the steps: working out the steps is the exercise. Level A applies the lesson, B combines it with earlier ones, C stretches it.
The self-checking notebook for this lesson is Dependence over time.
Common mistakes
What you will see when it goes wrong, why it happens, and the fix.
Where it’s used
Where this lesson’s ideas turn up in real work.