18179413. IDENTIFICATION OF SEASONAL LENGTH FROM TIME SERIES DATA simplified abstract (International Business Machines Corporation)
Contents
- 1 IDENTIFICATION OF SEASONAL LENGTH FROM TIME SERIES DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 IDENTIFICATION OF SEASONAL LENGTH FROM TIME SERIES DATA - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
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 18179413 titled 'IDENTIFICATION OF SEASONAL LENGTH FROM TIME SERIES DATA
Simplified Explanation
The method described in the patent application involves detecting seasonality in time series data by analyzing the power spectrum of the data and identifying peaks that represent seasonal patterns. Here are the key points of the innovation:
- Receiving a set of time series data
- Analyzing the data to generate a power spectrum
- Selecting peaks in the power spectrum
- Performing interpolation around the selected peaks
- Identifying peaks within a selected proportion of the peak power
- Determining seasonal length based on the identified peaks
Potential Applications
This technology could be applied in various fields such as finance, weather forecasting, and sales forecasting to identify seasonal patterns in data.
Problems Solved
This technology helps in identifying and analyzing seasonal patterns in time series data, which can be challenging to detect using traditional methods.
Benefits
The method provides a systematic approach to detect seasonality in time series data, allowing for better forecasting and decision-making based on seasonal patterns.
Potential Commercial Applications
Potential commercial applications of this technology include developing software tools for data analysis, offering consulting services for businesses looking to improve their forecasting accuracy, and integrating the method into existing data analysis platforms.
Possible Prior Art
One possible prior art for this technology could be existing methods for analyzing power spectra in time series data to identify patterns and trends.
What are the limitations of this method in detecting seasonality in time series data?
The method may struggle with detecting seasonality in noisy data or data with irregular patterns that do not conform to traditional seasonal cycles.
How does this method compare to existing techniques for analyzing seasonality in time series data?
This method offers a more systematic approach to identifying seasonal patterns by focusing on peaks in the power spectrum, which may provide more accurate and reliable results compared to traditional methods.
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.
