Abstract: Computer-implemented methods, systems, and computer readable media are provided for identifying and optimizing performance drivers of a healthcare related system. Healthcare related data may be analyzed to produce performance information pertaining to performance indicators for performance drivers of the healthcare related system. From the performance information, changes in the sets of performance indicators over time for the performance drivers may be determined and performance drivers with determined changes satisfying a threshold may be identified. An impact of the determined changes in the performance indicators to the identified performance drivers and contributions to the determined impact from one or more factors may be identified. Factors of the identified performance drivers with opposing utilization trends may be identified and an impact of the identified factors on the performance drivers may be determined.

Abstract: Computer-implemented methods, systems, and computer readable media are provided for identifying and optimizing performance drivers of a healthcare related system. Healthcare related data may be analyzed to produce performance information pertaining to performance indicators for performance drivers of the healthcare related system. From the performance information, changes in the sets of performance indicators over time for the performance drivers may be determined and performance drivers with determined changes satisfying a threshold may be identified. An impact of the determined changes in the performance indicators to the identified performance drivers and contributions to the determined impact from one or more factors may be identified. Factors of the identified performance drivers with opposing utilization trends may be identified and an impact of the identified factors on the performance drivers may be determined.

Abstract: Multi-step statistical modeling in one embodiment of the present disclosure enables anomaly detection, forecasting and/or root cause analysis of the energy consumption for a portfolio of buildings using multi-step statistical modeling. In one aspect, energy consumption data associated with a building, building characteristic data associated with the building, building operation and activities data associated with the building, and weather data are used to generate a variable based degree model. A base load factor, a heating coefficient and a cooling coefficient associated with the building and an error term are determined from the variable based degree model and used to generate a plurality of multivariate regression models. A time series model is generated for the error term to model seasonal factors which reflect monthly dependence on energy use and an auto-regressive integrated moving average model (ARIMA) which reflects temporal dependent patterns of the energy use.

Abstract: Structural changes in causal relationship over time may be detected, for example, by a Markov switching vector autoregressive model that detects and infers the structural changes in the causal graphs.

Abstract: Structural changes in causal relationship over time may be detected, for example, by a Markov switching vector autoregressive model that detects and infers the structural changes in the causal graphs.

Abstract: Multi-step statistical modeling in one embodiment of the present disclosure enables anomaly detection, forecasting and/or root cause analysis of the energy consumption for a portfolio of buildings using multi-step statistical modeling. In one aspect, energy consumption data associated with a building, building characteristic data associated with the building, building operation and activities data associated with the building, and weather data are used to generate a variable based degree model. A base load factor, a heating coefficient and a cooling coefficient associated with the building and an error term are determined from the variable based degree model and used to generate a plurality of multivariate regression models. A time series model is generated for the error term to model seasonal factors which reflect monthly dependence on energy use and an auto-regressive integrated moving average model (ARIMA) which reflects temporal dependent patterns of the energy use.

Abstract: Estimating monthly heating oil consumption of a building that uses heating oil and non-oil source of energy, may include separating by applying statistical models, yearly consumption of oil data associated with the building into base load oil consumption and space heating oil consumption. The separating may also include determining monthly base load oil consumption associated with the building. Monthly space heating consumption of oil may be estimated by applying a heating degree day density function to the space heating oil consumption. The monthly space heating consumption may be aggregated with the monthly base load oil consumption to estimate the monthly heating oil consumption.