Categorical Data Linear Effect Encoding for Time Series Forecasting
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Solution Overview
Problem
Categorical data in time series analysis often loses significant information regarding its effects on the series, making it difficult for users to understand and visualize these impacts effectively.
Innovation Solution
A system comprising a categorical data processing engine and visualization engine processes categorical data to generate linear effect values, which are used to improve forecast accuracy by better representing the effects of categorical events on time series data, incorporating 1-hot encoding and linear regression models to aggregate and display these effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If categorical data is represented in traditional formats, then data structure simplicity is maintained, but information loss about categorical effects occurs
Solution Approach 1:
The categorical data is segmented into multiple binary columns through one-hot encoding, where each category becomes a separate column with 0 or 1 values. This segmentation preserves all categorical information without loss while making the data suitable for linear model processing.
Solution Approach 2:
The patent transforms categorical data from its original format into linear effect values through parameter changes. By applying linear regression coefficients to one-hot encoded categories, the system converts categorical information into continuous parameters that represent their effects on the time series, thereby preserving information while enabling quantitative analysis.
2Ease of operation
If categorical data effects are aggregated into single values, then visualization simplicity is improved, but forecast accuracy deteriorates
Solution Approach 1:
The patent introduces a new dimension for representing categorical effects by creating separate linear effect value columns for each category. Instead of aggregating all effects into a single value, the system maintains multiple dimensions (one per category) that can be individually visualized and analyzed, thereby preserving forecast accuracy while enabling comprehensive visualization.
Solution Approach 2:
The solution nests multiple levels of information representation: one-hot encoded binary columns are nested within the broader categorical structure, and linear effect values are nested within the time series data framework. This nested structure allows both detailed individual category effects and aggregated overall effects to be accessed at different levels.
3Productivity
If traditional categorical representation is used, then data processing simplicity is maintained, but insight generation capability deteriorates
Solution Approach 1:
The patent introduces linear effect values as an intermediary representation between raw categorical data and forecast outcomes. These intermediary values serve as mediators that quantify the impact of each categorical event, making it easier to generate insights about how different categories affect the time series while systematically handling the processing complexity.
Data Source
AI summary
A method for processing categorical data in a time series includes: obtaining the time series where the time series includes a plurality of time series entries; generating, for each of the time series entries, a categorical data component and a supporting component; generating, using the supporting component and the categorical data component, a combined categorical data component specifying a linear effect of the categorical data component on each of the time series entries; predicting, using the combined categorical data component, a future forecast of the categorical data of the time series; and displaying, on a display, the future forecast of the categorical data to a user.