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Time Series Analysis Flashcards

7 cards from real Data Science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

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  1. In ARIMA(p, d, q), what does the parameter 'd' represent?

    Answer: The number of times the data is differenced to achieve stationarity

    The 'd' parameter in ARIMA is the integration order — it specifies how many times the series must be differenced to remove non-stationarity such as trends.

  2. The Augmented Dickey-Fuller (ADF) test is primarily used to:

    Answer: Test whether a time series has a unit root and is non-stationary

    The ADF test checks for a unit root in the series — rejecting the null hypothesis (unit root present) provides evidence that the series is stationary.

  3. In simple exponential smoothing, the smoothing parameter α controls:

    Answer: The rate at which the influence of older observations decays

    α (between 0 and 1) determines how quickly past observations lose influence — a higher α means more weight on recent data and faster adaptation to changes.

  4. What is the primary purpose of time series decomposition?

    Answer: To separate the series into trend, seasonality, and residual components

    Time series decomposition breaks a series into its underlying components — trend, seasonal pattern, and irregular residuals — to better understand and model each element separately.

  5. A simple moving average of order k is computed as:

    Answer: The arithmetic mean of the k most recent observations

    A simple moving average of order k computes the unweighted arithmetic mean of the k most recent data points, smoothing short-term fluctuations to reveal underlying trends.

  6. The Holt-Winters (Triple Exponential Smoothing) method is designed to handle:

    Answer: Both trend and seasonality simultaneously in a time series

    Holt-Winters extends simple exponential smoothing with three smoothing equations — level, trend, and seasonality — enabling it to forecast series exhibiting both patterns.

  7. Which forecasting accuracy metric is most appropriate when you want a scale-independent measure suitable for comparing across datasets with different magnitudes?

    Answer: Mean Absolute Percentage Error (MAPE)

    MAPE expresses forecast errors as a percentage of actual values, making it dimensionless and directly comparable across datasets of different scales.