18131337. DYNAMICALLY CONTROLLED PROCESSING OF TIME SERIES DATASETS BASED ON THE DETECTION OF STATIONARY TIME SERIES GRAINS simplified abstract (Microsoft Technology Licensing, LLC)
DYNAMICALLY CONTROLLED PROCESSING OF TIME SERIES DATASETS BASED ON THE DETECTION OF STATIONARY TIME SERIES GRAINS
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
Nazmiye Ceren Abay of Bothell WA (US)
Nikolay Sergeyevich Rovinskiy of Redmond WA (US)
Dhawal Dilip Parkar of Bellevue WA (US)
Vijaykumar Kuberappa Aski of Bellevue WA (US)
Neil Tenenholtz of Cambridge MA (US)
DYNAMICALLY CONTROLLED PROCESSING OF TIME SERIES DATASETS BASED ON THE DETECTION OF STATIONARY TIME SERIES GRAINS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18131337 titled 'DYNAMICALLY CONTROLLED PROCESSING OF TIME SERIES DATASETS BASED ON THE DETECTION OF STATIONARY TIME SERIES GRAINS
The disclosed techniques involve dynamically controlling select functions applied to time series datasets based on detecting stationary time series grains. In some cases, a system selectively applies functions like differencing based on the number of stationary time series grains meeting certain criteria compared to a threshold. By adjusting the differencing function based on the detection of stationary time series grains, the system enhances the accuracy and efficiency of systems using time series datasets.
- Selective application of functions to time series datasets based on stationary time series grains detection
- Criteria-based decision-making for applying functions like differencing
- Enhancing accuracy and efficiency of systems utilizing time series datasets
- Dynamic control of functions based on the number of stationary time series grains meeting criteria
- Improving machine learning systems and other applications using time series data
- Potential Applications:**
The technology can be applied in various fields such as finance, healthcare, weather forecasting, and industrial processes where time series data analysis is crucial.
- Problems Solved:**
The technology addresses the challenge of optimizing the application of functions to time series datasets by dynamically adjusting based on the detection of stationary time series grains.
- Benefits:**
- Increased accuracy in analyzing time series data - Enhanced efficiency in processing large datasets - Improved performance of machine learning systems
- Commercial Applications:**
The technology can be utilized in financial institutions for market analysis, in healthcare for patient monitoring, and in manufacturing for predictive maintenance, among other commercial applications.
- Questions about the Technology:**
1. How does the technology improve the accuracy of machine learning systems using time series datasets?
- The technology enhances accuracy by selectively applying functions based on the detection of stationary time series grains, optimizing data analysis.
2. What are the potential applications of this technology beyond machine learning systems?
- The technology can be applied in various industries such as finance, healthcare, and manufacturing for data analysis and predictive modeling.
Original Abstract Submitted
The disclosed techniques pertain to the dynamic control of select functions that are applied to a time series dataset based on the detection of stationary time series grains. In some configurations, a system selectively applies select functions, e.g., the application of a differencing function, to a dataset in response to determining that a number of stationary time series grains detected in the dataset meets one or more criteria with respect to a threshold. If a system determines that the number of stationary time series grains meets one or more criteria with respect to a threshold, the system can apply a differencing function to the entire dataset. By controlling the differencing function based on the detection of stationary time series grains with respect to a threshold, the system can increase the accuracy and the efficiency of a machine learning system or any other system that utilizes time series datasets.
