View source for 18504214. APPARATUS FOR NON-DETERMINISTIC FUTURE STATE PREDICTION USING TIME SERIES DATA AND OPERATION METHOD THEREOF simplified abstract (ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE)

=APPARATUS FOR NON-DETERMINISTIC FUTURE STATE PREDICTION USING TIME SERIES DATA AND OPERATION METHOD THEREOF=


==Organization Name==

[[:Category:ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE|ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE]]


[[Category:ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE]]

==Inventor(s)==

[[:Category:Hwin Dol Park of Daejeon (KR)|Hwin Dol Park of Daejeon (KR)]][[Category:Hwin Dol Park of Daejeon (KR)]]

[[:Category:Do Hyeun Kim of Daejeon (KR)|Do Hyeun Kim of Daejeon (KR)]][[Category:Do Hyeun Kim of Daejeon (KR)]]

[[:Category:Jae Hun Choi of Daejeon (KR)|Jae Hun Choi of Daejeon (KR)]][[Category:Jae Hun Choi of Daejeon (KR)]]

==APPARATUS FOR NON-DETERMINISTIC FUTURE STATE PREDICTION USING TIME SERIES DATA AND OPERATION METHOD THEREOF - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18504214 titled 'APPARATUS FOR NON-DETERMINISTIC FUTURE STATE PREDICTION USING TIME SERIES DATA AND OPERATION METHOD THEREOF

The patent application describes an apparatus that includes a preprocessor and a learner to predict future states of time series data.

* The preprocessor generates raw data, preprocesses it into time series data, and further preprocesses it into learning data.
* The learner uses the preprocessed learning data to train a prediction model that increases the similarity between predicted future states within the same cluster and decreases the similarity between predicted future states in different clusters.
* The prediction model is a machine learning model for predicting future states of time series data at any time point.

Potential Applications:
- Predictive maintenance in industrial machinery
- Forecasting stock prices in financial markets
- Predicting patient outcomes in healthcare settings

Problems Solved:
- Improving accuracy of future state predictions
- Enhancing clustering of similar data points
- Optimizing machine learning models for time series data

Benefits:
- Increased efficiency in decision-making processes
- Enhanced predictive capabilities for various industries
- Improved resource allocation based on future state predictions

Commercial Applications:
Title: "Enhanced Predictive Modeling for Time Series Data"
This technology can be utilized in industries such as manufacturing, finance, and healthcare to improve forecasting accuracy and optimize operations.

Prior Art:
Researchers have explored similar approaches in predictive modeling for time series data, focusing on clustering techniques and machine learning algorithms.

Frequently Updated Research:
Ongoing research in machine learning and time series analysis continues to refine predictive modeling techniques for various applications.

Questions about Enhanced Predictive Modeling for Time Series Data:
1. How does this technology compare to traditional forecasting methods?
2. What are the key challenges in implementing this predictive modeling approach in real-world scenarios?


==Original Abstract Submitted==

Disclosed is an apparatus, which includes a preprocessor that generates raw data, generates preprocessed time series data, and generates preprocessed learning data, and a learner that receives the preprocessed learning data as input data and trains a prediction model such that the similarity between a first future state predicted using the input data and a second future state predicted using data included in the same cluster as the input data increases and such that the similarity between the first future state and a third future state predicted using data included in a different cluster from the input data decreases, and the prediction model is a machine learning model for predicting a future state of the time series data at an arbitrary time point.



[[Category:G06N3/08]]
[[Category:CPC_G06N3/08]]