Microsoft technology licensing, llc (20240338290). DYNAMICALLY CONTROLLED PROCESSING OF TIME SERIES DATASETS BASED ON THE DETECTION OF STATIONARY TIME SERIES GRAINS simplified abstract

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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 20240338290 titled 'DYNAMICALLY CONTROLLED PROCESSING OF TIME SERIES DATASETS BASED ON THE DETECTION OF STATIONARY TIME SERIES GRAINS

    • Simplified Explanation:**

The patent application discusses techniques for dynamically controlling functions applied to time series datasets based on the detection of stationary time series grains. By selectively applying functions like differencing based on the number of stationary time series grains meeting certain criteria, the system aims to enhance the accuracy and efficiency of systems utilizing time series datasets.

    • Key Features and Innovation:**
  • Dynamic control of functions applied to time series datasets.
  • Detection of stationary time series grains to trigger selective application of functions.
  • Criteria-based decision-making for applying functions like differencing.
  • Improved accuracy and efficiency of machine learning systems using time series datasets.
    • 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 to improve the performance of machine learning systems and other applications relying on such data.

    • Benefits:**
  • Enhanced accuracy in analyzing time series data.
  • Increased efficiency in processing large datasets.
  • Improved performance of machine learning systems.
    • Commercial Applications:**
  • Title: "Dynamic Function Control for Time Series Data Analysis"
  • Potential commercial uses include financial market analysis, medical data interpretation, predictive maintenance in industries, and algorithmic trading systems.
  • Market implications include improved decision-making, reduced errors, and enhanced predictive capabilities.
    • Prior Art:**

Readers can explore prior research on time series data analysis, machine learning algorithms, and dynamic function control in data processing systems to understand the existing knowledge in this field.

    • Frequently Updated Research:**

Stay updated on advancements in machine learning techniques, time series data analysis algorithms, and applications of dynamic function control in various industries to leverage the latest developments in the field.

    • Questions about Dynamic Function Control for Time Series Data Analysis:**

1. How does the technology detect stationary time series grains in datasets? 2. What are the specific criteria used to determine the application of functions like differencing in the dataset?


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.

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