18373417. Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts simplified abstract (GOOGLE LLC)

Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

Organization Name

GOOGLE LLC

Inventor(s)

Sercan Omer Arik of San Francisco CA (US)

Yihe Dong of New York NY (US)

Qi Yu of San Diego CA (US)

Rui Wang of Mountain View CA (US)

Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts - A simplified explanation of the abstract

This abstract first appeared for US patent application 18373417 titled 'Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

Simplified Explanation

The Koopman Neural Forecaster (KNF) is a deep sequence model for time series forecasting that leverages deep neural networks to learn the linear Koopman space and coefficients of chosen measurement functions. KNF incorporates inductive biases for improved robustness against distributional shifts, using both a global operator to learn shared characteristics and a local operator to capture changing dynamics. It also includes a specially-designed feedback loop to continuously update the learned operators over time for rapidly varying behaviors, resulting in superior performance on multiple time series datasets suffering from distribution shifts.

• Deep sequence model for time series forecasting
• Utilizes deep neural networks to learn linear Koopman space and coefficients of measurement functions
• Incorporates inductive biases for improved robustness against distributional shifts
• Includes global operator to learn shared characteristics and local operator to capture changing dynamics
• Features a feedback loop to continuously update learned operators for rapidly varying behaviors

Potential Applications

The technology can be applied in various fields such as finance, weather forecasting, energy consumption prediction, and industrial process control.

Problems Solved

1. Improved forecasting accuracy in time series data suffering from distribution shifts 2. Enhanced adaptability to rapidly changing behaviors in dynamic systems

Benefits

1. Superior performance in time series forecasting 2. Increased robustness against distributional shifts 3. Continuous learning and updating of operators for dynamic behaviors

Potential Commercial Applications

"Deep Sequence Model for Time Series Forecasting: Applications in Finance and Energy Prediction"

Possible Prior Art

There may be prior art related to deep learning models for time series forecasting, such as LSTM networks and ARIMA models.

How does KNF compare to other deep learning models in terms of forecasting accuracy?

KNF's performance compared to other deep learning models in forecasting accuracy is not explicitly mentioned in the abstract. Further research or experimentation may be needed to determine its effectiveness relative to other models.

What are the computational requirements for implementing KNF in real-world applications?

The abstract does not provide information on the computational resources needed to implement KNF. Understanding the computational requirements is crucial for assessing the feasibility of deploying this technology in practical settings.

Original Abstract Submitted

Aspects of the disclosure provide a deep sequence model, referred to as Koopman Neural Forecaster (KNF), for time series forecasting. KNF leverages deep neural networks (DNNs) to learn the linear Koopman space and the coefficients of chosen measurement functions. KNF imposes appropriate inductive biases for improved robustness against distributional shifts, employing both a global operator to learn shared characteristics, and a local operator to capture changing dynamics, as well as a specially-designed feedback loop to continuously update the learnt operators over time for rapidly varying behaviors. KNF achieves superior performance on multiple time series datasets that are shown to suffer from distribution shifts.