Google llc (20240223209). Lossy Compression of Time Series Data simplified abstract
Contents
- 1 Lossy Compression of Time Series Data
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Lossy Compression of Time Series Data - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about the Technology
- 1.11 Original Abstract Submitted
Lossy Compression of Time Series Data
Organization Name
Inventor(s)
Jeffrey Max Quinlan-galper of Issaquah WA (US)
Lossy Compression of Time Series Data - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240223209 titled 'Lossy Compression of Time Series Data
Simplified Explanation
The method described in the patent application involves analyzing time series data to determine if it meets a certain size threshold. If the data is too small, a range of data points is identified, and each point is assigned a score. Based on these scores, certain data points are removed to reach the threshold size.
Key Features and Innovation
- Analysis of time series data to determine if it meets a size threshold
- Assigning scores to data points within a range to decide which points to remove
- Iterative process to adjust the data set to meet the threshold size
Potential Applications
This method could be applied in various fields where analyzing time series data is crucial, such as finance, healthcare, and environmental monitoring.
Problems Solved
This technology addresses the issue of dealing with time series data that is too small to be effectively analyzed, ensuring that only relevant data points are included in the analysis.
Benefits
- Improved accuracy in analyzing time series data
- Efficient removal of unnecessary data points
- Enhanced decision-making based on more robust data sets
Commercial Applications
The technology could be utilized in financial forecasting, medical research, and predictive maintenance in industrial settings. Its ability to optimize data sets could lead to more accurate predictions and insights.
Questions about the Technology
How does this method improve the analysis of time series data?
This method enhances the analysis by ensuring that only relevant data points are included, leading to more accurate results.
What industries could benefit most from this technology?
Industries such as finance, healthcare, and manufacturing could benefit greatly from this technology's ability to optimize data sets for analysis.
Original Abstract Submitted
a method includes obtaining time series data that includes a series of data points listed in temporal order. the method includes determining that a size of the time series data fails to satisfy a threshold size. in response, the method includes determining a range of the series of data points and determining, using the range of the series of data points, a respective score for each respective data point in the series of data points. the method also includes removing, using the respective scores for each data point in the series of data points, a plurality of data points from the series of data points. after removing the plurality of data points from the series of data points, the method includes determining an updated size of the series of data points and determining that the updated size of the series of data points satisfies the threshold size.
