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Forecasting time series

Trend, seasonality, honest baselines and time-based testing, using NASA temperatures and bike rentals from the hub.

Free on glitchdata intermediate 4 lessons 1 hr

What you'll learn

  • Identify trend, seasonality and noise in a series
  • Build simple baselines every forecast must beat
  • Test forecasts with time-based splits
  • Train a machine learning model on lag and calendar features

About this course

Forecasting — predicting what comes next from what came before — is one of the most common uses of data in business. This course uses two hub datasets, NASA GISTEMP global temperatures and Capital Bikeshare rentals, to cover the essentials that apply whatever tool you use.

Install with pip install pandas scikit-learn.

Before you start

  • Working with datasets for AI (or equivalent pandas experience)

Course content

4 lessons · 1 hr

  1. 1
    Trend, seasonality and noise

    Look at a series before forecasting it.

    Free preview 14 min
  2. 2
    Baselines every forecast must beat

    Naive and seasonal-naive forecasts, and how to score them.

    14 min
  3. 3
    Testing forecasts honestly

    Time-based splits, rolling evaluation and leakage.

    14 min
  4. 4
    Machine learning for forecasting

    Lag and calendar features with a gradient-boosted model.

    18 min

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