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- A strategy to assess water meter performancePublication . Cordeiro, Clara; Borges, Ana; Ramos, Maria do RosárioApparent water losses can be problematic to water companies’ revenues. This type of loss is very difficult to detect and quantify and is often associated with water meter anomalies. This study was motivated by a water company’s challenge that links a decrease in water consumption to water meters’ malfunction. The aim is to develop a strategy to detect decreasing water usage patterns, contributing to meter performance assessment. The basis of the approach is a combination of statistical methods. First, the time series of billed water consumption is decomposed using Seasonal-Trend decomposition based on Loess. Next, breakpoint analysis is performed on the seasonally adjusted time series. After that, the Mann–Kendall test and Sen’s slope estimator are used to analyze periods of progressive decrease changes in water consumption, defined by breakpoints. A quantitative indicator of this change is proposed. The strategy was successfully applied to eight-time series of water consumption from the Algarve, Portugal.
- Improving forecasting by resampling STL decompositionPublication . Cordeiro, Clara; Ramos, Maria do Rosário; Neves, M. ManuelaThe development of new forecasting algorithms has shown an increasing interest due to the emerging of new fields of application like machine learning and forecasting competitions. Although initially intended for independent random variables, bootstrap methods can be successfully applied to time series. The Boot.EXPOS procedure, which combines bootstrap and exponential smoothing methods, has shown promising results for forecasting. This work proposes a new approach to forecasting, which is briefly described as follows: using Seasonal-Trend decomposition by Loess (STL), the best STL fit is selected by testing all possible combinations of parameters. The best combination of smoothing parameters is chosen based on an accuracy measure. The time series is then decomposed into components according to the best STL fit. The Boot.EXPOS procedure is employed to forecast the seasonal component and the seasonally adjusted time series. These forecasts are aggregated to obtain a final forecast. The performance of this combined forecast is evaluated using real datasets and compared with other established forecasting methods.
- Trend tests: a tendency to resamplingPublication . Ramos, Maria do Rosário; Clara, CordeiroTrend analysis is an important problem in time series. Many studies have been developed to investigate this issue, with special attention to its application to environmental and hydrological time series. The presence of autocorrelation and missing observations affects the significance and power of trend tests, parametric or non-parametric. This study assesses the performance of two trend tests, t-test and the Mann-Kendall through an appropriate resampling technique. A new procedure based onsubsampling is proposed in order to assure good statistical properties of these tests. A comparison was established between this new approach and others already developed, such as bootstrap-based tests. In order to evaluate the performance of the new method, a simulation study is conducted considering a set of underlying slopes, different values of autocorrelation and different fractions of randomly missing data. The order of autocorrelation structure is estimated by the best fitting model obtained through the Akaike information criterion. Inspection of the data to detect missing observations is required, before applying the trend tests. In case of missing observations, their estimation and replace is performed by an imputation method available in software.
- Trend tests in time series with missing values: a case study with imputationPublication . Ramos, Maria do Rosário; Cordeiro, ClaraTesting for trend is an important problem, especially when one is dealing with environmental time series. The tests considered here are the usual t-test and the Mann-Kendall test, a nonparametric version widely used because it requires fewer assumptions. The aim is to assess the performance of two trend tests in time series with autocorrelation after an imputation method is applied to estimate the missing observations. The performance of the trend tests will be illustrated for some well-known data sets existing in R software.