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Service organizations may even use the big data and forecasting to detect errors, e.g., if an internet shop is implemented using micro services.
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For example: it contributes to overcoming cognitive biases and linear forecasting; it helps to detect weak signals, those even experts may not be aware of; it provides a way to see through technological hypes, promises and interests.
The developed method to estimate system accessibility constitutes a valuable tool to forecast system performance, to detect limiting resources and system hardware failures.
For the most accurate HR, based on performance metrics, we performed a forecast skill of HR to detect floods and droughts, comparing the results with in-situ observations.
Forecast errors are monitored to detect the injected outbreaks.
Here, we evaluate the potential benefit of using a complementary and independent dataset to forecast ILI and eventually to detect influenza epidemics in France.
In this context, one-step-ahead forecasts, especially when syndromic information is incorporated into the forecasting model, can be used to detect high-risk areas for outbreaks and, consequently, to develop efficient targeted surveillance [ 22].
Adaptive CUSUM control chart, which can continuously adjust itself by a one-step forecasting operator, has been proposed to detect efficiently and robustly for a range of mean shifts in the early literature.
It is clear that rCBV results thus in a more predictive and sensible method to detect and forecast future structural and functional alterations that will appear only many months later.
Using a disease surveillance system, one is able to apply statistical methods, such as cumulative sum (CuSum) [ 9, 10] or autoregressive integrated moving average (ARIMA) [ 11- 15], in order to forecast an epidemic curve or to detect aberrations in disease spread.
In [41], a method based on short term state forecasting is proposed that is able to detect false data injection in smart grids.
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