Anomaly Detection from Timeseries Statistics (Water Management)

Using Timeseries Statistics for the Detection of Anomalies

To address the challenges of identifying anomalies in water monitoring data, I developed an approach that leverages statistical summaries of timeseries data. Experts often manually analyze datasets like groundwater levels or water quality metrics, a process that can be time-consuming and prone to errors. To improve this, I focused on automating the task by using key statistical measures - such as mean, standard deviation, variance, kurtosis, skewness, and percentiles - along with visualizations like boxplots, to summarize data across defined time periods (e.g., yearly intervals).

The approach was designed to simplify anomaly detection by enabling direct comparisons of entire periods at once. By summarizing the statistics for each period, the solution made extreme outliers - such as unusually high or low values - stand out clearly in the context of long-term trends. This method of period-by-period summarization allowed users to quickly identify anomalies without needing to dig through the raw data manually, making it a powerful tool for early outlier detection and more efficient data quality reviews.

I showcased these methods through interactive Jupyter Notebooks, allowing users to visualize timeseries data, spot trends, and identify unusual patterns with ease. This approach provided a user-friendly way to summarize large datasets, helping experts focus on deriving meaningful insights while significantly reducing the time spent on data quality assessments. Ultimately, it contributed to enhancing the monitoring and evaluation of water systems, improving both the accuracy and efficiency of data quality assurance efforts.