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Evaluating models that rely on aggregate historical data

A technology of data sets and topic models, applied in the field of evaluating models that rely on aggregated historical data, can solve problems such as no repeatable standards

Pending Publication Date: 2019-12-27
GOOGLE LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, for many complex systems, there is no known repeatable standard

Method used

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  • Evaluating models that rely on aggregate historical data
  • Evaluating models that rely on aggregate historical data
  • Evaluating models that rely on aggregate historical data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0022] Complex systems involving a large number of variables include, for example, weather forecasting systems, market analysis systems, traffic forecasting systems, elevator demand forecasting systems, and the like. Often, these (and other) complex systems can be affected by a number of different factors. For example, weather forecasts use models based on variables such as precipitation levels, humidity, air pressure, temperature, wind speed, and movement of transition fronts. While it is easy to know whether the forecast was accurate (e.g., did it rain when it was predicted to rain, or did it not), it can be difficult to know how much influence any one variable exerted (e.g., did it rain because the temperature dropped or vice versa?). Similarly, when it comes to advertising, it is difficult to know how much sales are being driven by ad spending, and more specifically, how much ad spending in one format (e.g., broadcast media) is compared to another (e.g., online advertising...

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Abstract

Systems and methods for model validation are described that includes generating a first and a second time series of segmentation states for a data set representative of a simulated population, e.g., acollection of membership counts corresponding to respective segments of the simulated population. The first and second time series of segmentation states are generated by respectively processing thedata set through a first and a second simulation each comprising iterative application of a plurality of event functions. The first and the second simulation differ in at least one capacity, e.g., oneincluding a first event function configured with a first parameter, and the second not. Analysis of differences between the first and second time series may be compared to analysis of one of the timeseries using a subject model. The comparison is then used to validate the model or demonstrate accuracies, inaccuracies, and / or model bias with respect to a performance metric.

Description

[0001] Cross references to related patent applications [0002] This application claims the benefit and priority of U.S. Patent Application No. 15 / 707,594, filed September 18, 2017, the entire disclosure of which is incorporated herein by reference. Background technique [0003] Complex systems involving a large number of variables can be affected by many different factors. Models have been developed to attempt to measure or quantify the impact of various factors. However, these models cannot control for the influence of other factors. As a result, the validity or accuracy of these models and the validity or accuracy of estimates or predictions generated by these models can vary. These models are measurement tools that can be calibrated and adjusted. To calibrate a measurement tool, the tool should be applied to a standard of known accuracy. However, for many complex systems, there is no known repeatable standard. [0004] This problem can be seen, for example, in models ...

Claims

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Application Information

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IPC IPC(8): G06Q30/02
CPCG06Q30/0201G06Q30/02G06F17/18G06F17/16G06F16/2465G06F16/244
Inventor S.张J.瓦弗
Owner GOOGLE LLC