Time-series analysis in monitoring - trends, seasonality, anomalies
In environmental monitoring a single reading means little - real knowledge is born when the measurements form a time series. Only a series shows whether something is rising, falling, returning each year, or has suddenly “shot up”. It is worth knowing what to look for in such a chart.
Three things we read from a series
- Trend - the long-term direction (e.g. a slowly rising phosphorus concentration over the years). It answers the question “are things getting better or worse”.
- Seasonality - a repeating annual or daily rhythm (e.g. summer minima of oxygen, night-time drops, spring blooms). It has to be recognised so as not to confuse it with a trend.
- Anomalies - single, unusual deviations (e.g. a spike in turbidity after heavy rain). Some are real events, some are measurement errors.
Simple interpretation tools
- A chart of values over time - the starting point; even a glance tells a lot.
- A moving average - smooths out the noise and brings out the trend.
- Comparing seasons (year to year, the same month) - separates the annual rhythm from a long-term change.
- Setting against context - weather, rainfall, water level (see combining with public data).
The most common mistakes
- Confusing seasonality with a trend - “rising” in July does not mean “rising year on year”.
- Uneven intervals / gaps - they affect averages and charts; it is worth marking them, not hiding them.
- Reacting to a single point - an anomaly is only confirmed by the next measurement or by context.
- A “categorical” time axis - dates must be treated as time (proportionally), not as labels.
In practice
Good analysis starts with an orderly, continuous series at fixed points. In LimnoLog measurements immediately form charts over time (with an option to compare seasons), and the full dataset you can export to Excel or prepare for statistical analysis when you need deeper methods.
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