Size Of Historical Data Research Articles

Data-driven chance constrained stochastic program

In this paper, we study data-driven chance constrained stochastic programs, or more specifically, stochastic programs with distributionally robust chance constraints (DCCs) in a data-driven setting to provide robust solutions for the classical chance constrained stochastic program facing ambiguous probability distributions of random parameters. We consider a family of density-based confidence sets based on a general $$\phi $$ź-divergence measure, and formulate DCC from the perspective of robust feasibility by allowing the ambiguous distribution to run adversely within its confidence set. We derive an equivalent reformulation for DCC and show that it is equivalent to a classical chance constraint with a perturbed risk level. We also show how to evaluate the perturbed risk level by using a bisection line search algorithm for general $$\phi $$ź-divergence measures. In several special cases, our results can be strengthened such that we can derive closed-form expressions for the perturbed risk levels. In addition, we show that the conservatism of DCC vanishes as the size of historical data goes to infinity. Furthermore, we analyze the relationship between the conservatism of DCC and the size of historical data, which can help indicate the value of data. Finally, we conduct extensive computational experiments to test the performance of the proposed DCC model and compare various $$\phi $$ź-divergence measures based on a capacitated lot-sizing problem with a quality-of-service requirement.

On Optimizing Anomaly Detection Rules for Gas Turbine Health Monitoring

Gas turbine health monitoring is a critical process in preventing costly unplanned maintenance and secondary damage. To monitor gas turbine health, control signals are typically collected and analyzed using anomaly detection rules and models to assess failure likelihood based on observed data patterns. An analytic designer will often deal with rule optimization tasks in order to maximize failure detection and reduce false alarms. Manual tradeoff analysis is typically time consuming and suboptimal. In this paper, we attempt to address this issue by introducing a strategy for automatic and efficient rule optimization. By focusing on optimizing rule parameters while keeping rule structure intact, we maximize the rule performance by integrating domain knowledge with data driven optimization techniques. Realizing that automated rule tuning can be computationally expensive and infeasible to complete in reasonable time, we will leverage our recently-developed scalable learning framework - iScale that allows for automatically distributing rule tuning tasks to a large number of cloud computers, which not only dramatically speeds up tuning process, but also enables us to handle big size of historical data for tuning. We also explore different search methods to make rule tuning more efficient and effective and finally demonstrate our rule optimization strategy by a real-world application.

A Study on the Construction of Past Travel Time Pattern for Freeway Travel Time Forecasting—Focused on Loop Detectors -

The objective of this study is to propose methods for determining optimal representative value and the optimal size of historical data for reliable travel time forecasting. We selected values with the smallest mean of forecasting errors as the optimal representative value of travel time pattern data. The optimal size of historical data used was determined using the CVMSE (Cross Validated Mean Square Error) method. According to the results of applying the methods to point vehicle detection data of Korea Highway Corporation, the optimal representative values were analyzed to be the median. Because the mean used by previous researchers were influenced by each historical profile of travel time patterns, the distribution is easily skewed if there are very large or very small values in the travel time pattern data. Second, it was analyzed that 60 days' worth of data is the optimal size of historical data that should be used for travel time forecasting.