International business machines corporation (20240119116). IDENTIFICATION OF SEASONAL LENGTH FROM TIME SERIES DATA simplified abstract

IDENTIFICATION OF SEASONAL LENGTH FROM TIME SERIES DATA

Organization Name

international business machines corporation

Inventor(s)

David Alvra Wood, Iii of Scarsdale NY (US)

Petros Zerfos of New York NY (US)

Syed Yousaf Shah of Yorktown Heights NY (US)

IDENTIFICATION OF SEASONAL LENGTH FROM TIME SERIES DATA - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240119116 titled 'IDENTIFICATION OF SEASONAL LENGTH FROM TIME SERIES DATA

Simplified Explanation

The method described in the abstract is a technique for detecting seasonality in time series data by analyzing the power spectrum of the data and identifying peaks that represent seasonal patterns.

• Analyzing time series data to generate a power spectrum, which shows power as a function of frequency.
• Selecting a peak in the power spectrum with peak power.
• Performing interpolation around the selected peak and selecting additional peaks with powers within a selected proportion of the peak power.
• Identifying the selected peak as representing a season if the number of additional peaks is below a threshold number.
• Determining the seasonal length based on the frequency at the identified peak.

Potential Applications

This technology can be applied in various fields such as finance, meteorology, and economics to identify seasonal patterns in data and make informed decisions based on these patterns.

Problems Solved

This technology helps in identifying and understanding seasonal trends in time series data, which can be crucial for forecasting, planning, and decision-making in various industries.

Benefits

- Improved accuracy in forecasting and decision-making. - Better understanding of seasonal patterns in data. - Enhanced ability to plan and strategize based on seasonal trends.

Potential Commercial Applications

Optimizing inventory management, predicting sales trends, improving resource allocation, and enhancing marketing strategies are some potential commercial applications of this technology.

Possible Prior Art

One possible prior art could be traditional methods of time series analysis that may not specifically focus on detecting seasonality in data.

What are the limitations of this technology in real-world applications?

The limitations of this technology in real-world applications may include: - Sensitivity to noise in the data, which can affect the accuracy of peak detection. - The need for expertise in interpreting the results and making informed decisions based on the identified seasonal patterns.

How does this technology compare to existing methods of detecting seasonality in time series data?

This technology offers a more systematic and data-driven approach to detecting seasonality by analyzing the power spectrum and identifying peaks that represent seasonal patterns. Compared to traditional methods, this approach may provide more accurate and reliable results in identifying seasonal trends in time series data.

Original Abstract Submitted

a method of detecting seasonality in time series data includes receiving a set of time series data, analyzing the time series data to generate a power spectrum, the power spectrum indicative of power as a function of frequency, and selecting a peak in the power spectrum, the selected peak having a peak power. the method also includes performing an interpolation around the selected peak, and selecting a number of additional peaks having powers within a selected proportion of the peak power. the method further includes, based on the number of additional peaks being less than a threshold number, identifying the selected peak as representing a season having a seasonal length, and determining a seasonal length of the season based on a frequency at the identified peak.