Abstract: Detecting network congestion at a network device by collecting, for various time intervals, measurements of a quantifiable characteristic of traffic transiting the network device at a plurality of respective measurement times during each of the time intervals. Described systems and methods select two or more sets of measurements, each set corresponding to a respective time interval; determine, for each of the selected sets of measurements, respective upper and lower boundaries for the quantifiable characteristic during the respective time interval; and analyze trends for a time window inclusive of the time intervals corresponding to the selected sets of measurements, the trends including an upper boundary trend and a lower boundary trend. Congestion is then identified responsive to the analysis detecting a substantially constant upper boundary trend concurrent with a rising lower boundary trend.

Abstract: A robust, simple, application-specific way to evaluate data models and forecasts is provided for evaluating whether a forecast is trustworthy. An approach for formulating a single, summary indication of data/model/forecast quality relevant for the task at hand is described. This approach includes generating a forecast model from collected data, combining multiple model-quality parameters based on the model, computing an indication based on the combination of parameters, and evaluating the model and forecast quality based on the indication. This indication, in the form of a Model Quality Index, can also be used to compare different types of models produced by different types of analysis approaches.

Abstract: A methodology for automatic a priori data pattern analysis is provided. Described methods allow consistent and objective determination of outliers; trend; seasonality; and level shifts; and the production of better models and more accurate forecasts. In addition, a two-step way to automatically determine seasonality and locate possible events in the data set is described. Decomposition of data into seasonal, trend and level components; detection of outliers and level-shift events in the time series based on statistical analysis of the time series; detection of seasonality based on statistical analysis of clusters of data, known as cluster-based seasonality analysis, or CBSA; evaluation of the goodness of fit of a model to data, using the existing goodness of fit indicator, R2; and seasonality analysis, using a sequence of cluster-based seasonality analysis (CBSA) and Fourier analysis are described.

Abstract: Evaluation of data models and forecasts is provided, enabling processing of large numbers of forecast scenarios in a production environment. An approach for optimizing the computation for statistical modeling and forecasting is described. This approach includes calculating a recommended number of collected data points, calculating a cap on time to elapse, deciding based on at least one of the recommended number of collected data points and the cap on time to elapse whether to generate a forecast model and generating a forecast model from the collected data points.