| 1: procedure DOW-FLH (Original Time Series) |
| 2: set flags for consecutive zeros ▹Handel Dirty Data |
| 3: for each day do |
| 4: find the time point index (TPI) of the lowest flow |
| 5: end for |
| 6: find TPIs’ median number as starts of daily seasons, e.g., 3 am |
| 7: for model in all DOW models do▹Build DOW Models |
| 8: take all seasons related to to a group |
| 9: remove flagged consecutive zeros |
| 10: calculate median of grouped seasons as the model |
| 11: end for |
| 12: for model in all DOW models do ▹Fit/Adapt to Get Scalings and Additions |
| 13: for each realted season do |
| 14: remove flagged consecutive zeros |
| 15: estimate by robustly fitting to the season |
| 16: rounding all values of the fitted model to integers as the season’s flow levels |
| 17: get residuals as the difference between the fitted and the season |
| 18: end for |
| 19: end for |
| 20: for each flow level Standardize Residuals (FLH) do▹Standardize Residuals (FLH) |
| 21: take all residuals for this flow level (or with neighbours if not enough) |
| 22: calculate standard deviations (STD) |
| 23: end for |
| 24: consider all STDs with all flow levels as the flow level heteroscedasticity (FLH) |
| 25: divide each residual with timely corresponding STD to standardize |
| 26: for each detection algorithm do ▹Detection |
| 27: feed the entire standardized residual time series to the algorithm |
| 28: get algorithm-specific anomalies or anomaly scores |
| 29: end for |
| 30: return the list of anomalies or anomaly scores |
| 31: end procedure |
