Time Series Analysis Guide
Analyze time-based data for trends, patterns, and forecasts.
Usage
- Visualize the raw time series to identify obvious patterns
- Decompose into components: trend (long-term direction), seasonality (repeating patterns), residual (noise)
- Check for stationarity (required for many forecasting methods)
- Choose an appropriate forecasting method based on data characteristics
- Validate forecasts with holdout data and appropriate error metrics
Examples
- Revenue trend analysis: Plot monthly revenue over 24 months. Apply 3-month moving average to smooth noise. Identify: upward trend ($10K/month growth), seasonal peaks (November-December for e-commerce), and anomalies (April dip = COVID, August spike = viral campaign). Separate the signal from the noise
- Seasonal decomposition: Daily website traffic shows: weekly pattern (dips on weekends), monthly pattern (spike at month-end for B2B), annual pattern (summer slowdown). Decompose using STL (Seasonal and Trend decomposition using LOESS) to isolate each component. Now you can forecast each separately and recombine
- Anomaly detection: Calculate rolling mean and standard deviation (30-day window). Flag any data point more than 2.5 standard deviations from the rolling mean. Alert on anomalies in real-time: sudden traffic drop (outage?), unusual spike (viral content? bot attack?), gradual drift (market shift?)
Guidelines
- Always plot the data first — visual inspection catches patterns that statistics miss
- Moving averages are the simplest and most robust method for trend identification. Start with 7-day (weekly patterns) or 30-day (monthly patterns)
- For forecasting: use exponential smoothing (Holt-Winters) for data with trend + seasonality. Use ARIMA for more complex patterns. Use Prophet (Facebook/Meta) for business data with holidays and changepoints
- Forecast accuracy degrades rapidly beyond 2-3 seasonal cycles — a model trained on 2 years of monthly data shouldn't forecast more than 6 months ahead
- Never extrapolate a trend indefinitely — all trends eventually change. Build scenario models (optimistic, base, pessimistic) instead
- Account for external events: promotions, holidays, competitors, market shifts. Pure time series models miss these — add them as features or adjust forecasts manually