Data Archive

Anomaly Detection in Time Series

Anomaly Detection in Time Series Data

Anomaly detection in time series data involves identifying observations that deviate significantly from expected temporal patterns. Because time series data is ordered and may include trends and seasonality, specialized methods are often required.

1. Statistical Methods

  • Moving Average / Rolling Statistics: Compute rolling mean or median and flag points that are significantly above or below expected values.
  • Seasonal Decomposition: Break the series into trend, seasonal, and residual components. Anomalies are typically detected in the residuals, which represent deviations from the expected pattern. Python’s seasonal_decompose (STL) can be used.
  • Z-score / IQR: Standardize residuals or raw values using Z-scores or the interquartile range to detect outliers.

2. Time Series Models

  • ARIMA / SARIMA: Fit models to capture autocorrelations and trends; anomalies are detected as large residuals.
  • State Space Models / ETS: Forecast future values and detect anomalies based on residual deviations.

3. Machine Learning Approaches

  • LSTM Networks: Train sequence models to predict next values; large prediction errors indicate potential anomalies.
  • Isolation Forest / One-Class SVM: Can be adapted for time series by encoding temporal features (e.g., lagged values).

4. Change Point Detection

  • Detect points where the statistical properties of the series shift (mean, variance, correlation), which may indicate structural anomalies. Methods include Bayesian change point detection or ruptures library.

5. Visual Methods

  • Time Series Plots: Interactive plots can help spot unusual spikes, drops, or patterns.
  • Control Charts: Define control limits around expected values; points outside limits are flagged.
  • Residual Plots: Plot residuals from models or decompositions to highlight anomalies.

6. Practical Workflow

  1. Explore the dataset: Check time range, missing values, and initial statistics.
  2. Decompose / model: Apply STL decomposition, ARIMA, or another time series model.
  3. Detect anomalies: Use residual-based methods (Z-score, IQR, model errors) or ML-based approaches.
  4. Visualize results: Highlight anomalies on interactive plots to interpret patterns.
  5. Validate anomalies: Compare against known events or domain knowledge to reduce false positives.

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