View source for 18112944. ENHANCED TIME SERIES FORECASTING simplified abstract (Snowflake Inc.)

=ENHANCED TIME SERIES FORECASTING=


==Organization Name==

[[:Category:Snowflake Inc.|Snowflake Inc.]]


[[Category:Snowflake Inc.]]

==Inventor(s)==

[[:Category:Michel Adar of Campbell CA (US)|Michel Adar of Campbell CA (US)]][[Category:Michel Adar of Campbell CA (US)]]

[[:Category:Boxin Jiang of Sunnyvale CA (US)|Boxin Jiang of Sunnyvale CA (US)]][[Category:Boxin Jiang of Sunnyvale CA (US)]]

[[:Category:Qiming Jiang of Redmond WA (US)|Qiming Jiang of Redmond WA (US)]][[Category:Qiming Jiang of Redmond WA (US)]]

[[:Category:John Reumann of Kirkland WA (US)|John Reumann of Kirkland WA (US)]][[Category:John Reumann of Kirkland WA (US)]]

[[:Category:Boyu Wang of Menlo Park CA (US)|Boyu Wang of Menlo Park CA (US)]][[Category:Boyu Wang of Menlo Park CA (US)]]

[[:Category:Jiaxun Wu of Sammamish WA (US)|Jiaxun Wu of Sammamish WA (US)]][[Category:Jiaxun Wu of Sammamish WA (US)]]

==ENHANCED TIME SERIES FORECASTING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18112944 titled 'ENHANCED TIME SERIES FORECASTING

==Simplified Explanation==

Using an attributes model, this patent application proposes a time series forecasting model that determines a set of features based on time series data, including periodic components. The data can be divided into segments, and each segment can be assigned a weight based on its age. This results in a set of weighted segments of time series data. 

The patent application also introduces a trend detection model that analyzes the weighted segments to determine trend data. By combining the set of features and the trend data, a time series forecast can be generated. 

* The patent application proposes a time series forecasting model based on an attributes model.
* The model determines a set of features from time series data, including periodic components.
* The time series data can be divided into segments, and each segment is assigned a weight based on its age.
* The weighted segments of time series data are analyzed using a trend detection model to determine trend data.
* The set of features and trend data are combined to generate a time series forecast.

==Potential Applications==
This technology has potential applications in various fields, including:

* Financial forecasting: Predicting stock prices, market trends, and economic indicators.
* Demand forecasting: Forecasting product demand, optimizing inventory management, and supply chain planning.
* Energy forecasting: Predicting energy consumption, optimizing energy production, and grid management.
* Weather forecasting: Forecasting weather patterns, improving climate models, and disaster preparedness planning.

==Problems Solved==
This technology addresses several problems in time series forecasting, such as:

* Incorporating periodic components: By considering periodic components in the set of features, the model can capture recurring patterns in the data.
* Weighting segments based on age: Assigning weights to segments based on their age helps prioritize recent data, which may be more relevant for forecasting.
* Trend detection: The trend detection model allows for the identification of underlying trends in the data, enabling more accurate forecasts.

==Benefits==
The benefits of this technology include:

* Improved accuracy: By considering both periodic components and trend data, the time series forecasts generated by this model are expected to be more accurate.
* Flexibility: The model can be applied to various types of time series data and can adapt to different forecasting needs.
* Efficiency: The use of weighted segments and trend detection helps optimize the forecasting process, reducing computational time and resources required.


==Original Abstract Submitted==

Using an attributes model of a time series forecasting model, determine a set of features based on time series data, the set of features including periodic components. The time series data may be divided into a set of segments. For each segment of the set of segments, a weight may be assigned using an age of the segment, resulting in a set of weighted segments of time series data. Using a trend detection model of the time series forecasting model, trend data from the set of weighted segments of time series data may be determined. A time series forecast may be generated by combining the set of features and the trend data.



[[Category:G06F17/18]]