Time Series Analysis of Irregularly Observed Data
E274131
"Time Series Analysis of Irregularly Observed Data" is a scholarly work by statistician Emanuel Parzen that develops methods for modeling and analyzing time series when observations occur at uneven or irregular time intervals.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Time Series Analysis of Irregularly Observed Data canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2515038 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Time Series Analysis of Irregularly Observed Data Context triple: [Emanuel Parzen, hasPublication, Time Series Analysis of Irregularly Observed Data]
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A.
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
"Extrapolation, Interpolation, and Smoothing of Stationary Time Series" is a foundational mathematical work by Norbert Wiener that developed the theory of optimal prediction and filtering for stationary stochastic processes, laying the groundwork for modern signal processing and control theory.
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B.
Sequential Analysis
Sequential Analysis is a foundational statistical methodology that develops procedures for evaluating data as it is collected, allowing decisions to be made at variable sample sizes rather than after a fixed number of observations.
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C.
Innovations approach to detection and estimation
"Innovations approach to detection and estimation" is a seminal work by Thomas Kailath that develops a powerful stochastic framework for solving signal detection and parameter estimation problems, particularly in control and communication systems.
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D.
Standard Time series
The Standard Time series is a collection of jazz albums by trumpeter Wynton Marsalis that explores and reinterprets classic jazz standards and traditional American music.
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E.
“A New Approach to Linear Filtering and Prediction Problems”
“A New Approach to Linear Filtering and Prediction Problems” is Rudolf E. Kálmán’s landmark 1960 paper that introduced the Kalman filter, a foundational algorithm for optimal estimation in control theory, signal processing, and navigation.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Time Series Analysis of Irregularly Observed Data Target entity description: "Time Series Analysis of Irregularly Observed Data" is a scholarly work by statistician Emanuel Parzen that develops methods for modeling and analyzing time series when observations occur at uneven or irregular time intervals.
-
A.
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
"Extrapolation, Interpolation, and Smoothing of Stationary Time Series" is a foundational mathematical work by Norbert Wiener that developed the theory of optimal prediction and filtering for stationary stochastic processes, laying the groundwork for modern signal processing and control theory.
-
B.
Sequential Analysis
Sequential Analysis is a foundational statistical methodology that develops procedures for evaluating data as it is collected, allowing decisions to be made at variable sample sizes rather than after a fixed number of observations.
-
C.
Innovations approach to detection and estimation
"Innovations approach to detection and estimation" is a seminal work by Thomas Kailath that develops a powerful stochastic framework for solving signal detection and parameter estimation problems, particularly in control and communication systems.
-
D.
Standard Time series
The Standard Time series is a collection of jazz albums by trumpeter Wynton Marsalis that explores and reinterprets classic jazz standards and traditional American music.
-
E.
“A New Approach to Linear Filtering and Prediction Problems”
“A New Approach to Linear Filtering and Prediction Problems” is Rudolf E. Kálmán’s landmark 1960 paper that introduced the Kalman filter, a foundational algorithm for optimal estimation in control theory, signal processing, and navigation.
- F. None of above. chosen
Statements (42)
| Predicate | Object |
|---|---|
| instanceOf |
scholarly work
ⓘ
statistical monograph ⓘ |
| aim |
to generalize classical time series methods to irregular observation times
ⓘ
to provide inference procedures for unequally spaced time series data ⓘ |
| appliesTo |
astronomical time series
ⓘ
biomedical time series ⓘ econometric time series ⓘ environmental time series ⓘ |
| author | Emanuel Parzen ⓘ |
| contribution |
extension of spectral analysis to irregular observation times
ⓘ
formal framework for time series with unequal spacing ⓘ methods to estimate autocovariance from irregular samples ⓘ tools for handling missing and intermittent observations in time series ⓘ |
| field |
statistics
ⓘ
time series analysis ⓘ |
| focusesOn |
estimation of dependence structure from irregular data
ⓘ
likelihood-based methods for irregular time series ⓘ linear models for irregularly observed processes ⓘ methods for modeling irregular observation times ⓘ spectral methods for irregularly spaced observations ⓘ statistical inference with irregular time indices ⓘ |
| hasAuthorProfession | statistician ⓘ |
| hasKeyConcept |
covariance structure under irregular sampling
ⓘ
estimation under nonuniform sampling ⓘ irregular time index ⓘ spectral representation of irregular series ⓘ unequal spacing of observations ⓘ |
| language | English ⓘ |
| relatedTo |
continuous-time time series models
ⓘ
covariance function estimation ⓘ irregular sampling in stochastic processes ⓘ spectral density estimation ⓘ time series with missing data ⓘ |
| topic |
autocorrelation estimation
ⓘ
covariance estimation ⓘ irregularly observed time series ⓘ nonstationary time series ⓘ sampling schemes for time series ⓘ spectral analysis ⓘ stationary time series ⓘ stochastic processes ⓘ unequally spaced time series ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Time Series Analysis of Irregularly Observed Data Description of subject: "Time Series Analysis of Irregularly Observed Data" is a scholarly work by statistician Emanuel Parzen that develops methods for modeling and analyzing time series when observations occur at uneven or irregular time intervals.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.