18397744. Lossy Compression of Time Series Data simplified abstract (Google LLC)
Contents
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 18397744 titled 'Lossy Compression of Time Series Data
The method described in the abstract involves analyzing time series data to identify and remove data points that do not meet a certain threshold size. By determining a range of data points and assigning scores to each point within that range, the method can effectively filter out irrelevant data. This process results in an updated series of data points that meets the required threshold size.
- The method involves analyzing time series data to identify and remove data points that do not meet a certain threshold size.
- By determining a range of data points and assigning scores to each point within that range, the method can effectively filter out irrelevant data.
- This process results in an updated series of data points that meets the required threshold size.
Potential Applications: - Data analysis and cleansing in various industries such as finance, healthcare, and manufacturing. - Improving the accuracy and reliability of predictive modeling and forecasting. - Enhancing the efficiency of data processing and decision-making processes.
Problems Solved: - Eliminating unnecessary data points that do not contribute to the overall analysis. - Streamlining the data cleaning process and improving the quality of the dataset. - Ensuring that the time series data meets specific size requirements for accurate analysis.
Benefits: - Increased accuracy and reliability of data analysis. - Improved efficiency in data processing and decision-making. - Enhanced predictive modeling and forecasting capabilities.
Commercial Applications: Title: Data Cleansing and Analysis Tool for Enhanced Decision-Making This technology can be utilized in various industries such as finance, healthcare, and manufacturing to improve data quality and accuracy. It can also be integrated into existing data analysis tools to streamline the data cleaning process and enhance predictive modeling capabilities.
Questions about Time Series Data Analysis: 1. How does the method determine the range of data points to analyze? The method determines the range of data points based on the size of the time series data and the threshold size requirement. 2. What are the potential implications of removing data points from the series? Removing irrelevant data points can improve the accuracy and reliability of the overall analysis by focusing on the most relevant data.
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.
