Time Series Analysis
MachineLearningTask AIO:TimeSeriesAnalysis NOT_REVIEWED
A machine learning task focused on methods for analyzing time series data to extract meaningful statistics and characteristics.
Ontology IRI:
https://w3id.org/aio/TimeSeriesAnalysis
Ontology Structure & Relationships
Parent Classes
Branches
Annotations & Sources
Definition Sources:
Subsets:
https://w3id.org/aio/MachineLearningSubset
Curated AIMech Concepts Grounded to this Class
12 Concepts| Curated Concept | Kind | Mapping Relation | Rationale |
|---|---|---|---|
Analytic moment-based Gaussian-process filtering
AIMech:AnalyticMomentBasedGaussianProcessFiltering |
Algorithms | related_match | The algorithm infers hidden states recursively from time-indexed noisy measurements. This is a concrete time-series analysis relationship; the complete filter includes measurement conditioning, rather than only prediction of future values. |
Correlation-based Hammerstein identification
AIMech:CorrelationBasedHammersteinIdentification |
Algorithms | related_match | The procedure estimates nonlinear dynamical structure from temporal input/output observations and lagged correlations. The pinned target is the analysis task; related mapping preserves the algorithm/task distinction. |
Correlation-based Wiener identification
AIMech:CorrelationBasedWienerIdentification |
Algorithms | related_match | The procedure estimates nonlinear dynamical structure from temporal input/output observations and lagged correlations. The pinned target is the analysis task; related mapping preserves the algorithm/task distinction. |
Dynamic topic model
AIMech:DynamicTopicModel |
Model Families | related_match | The model analyzes temporally organized document collections. The broader AIO time-series task differs in abstraction from this particular probability-model family. |
Hammerstein model
AIMech:HammersteinModel |
Model Families | related_match | The source develops this ordered input-output dynamical representation for analyzing and identifying nonlinear systems from temporal observations. The pinned target denotes the analysis task; a related model/task mapping preserves that distinction and does not assert that the cascade is a task, neural layer or particular estimator. |
Hammerstein–Wiener model
AIMech:HammersteinWienerModel |
Model Families | related_match | This ordered dynamical model family represents temporal input-output relationships for system identification and analysis. The inspected AIO target is an analysis task, so the link is a model/task relationship rather than exact identity or an asserted subclass. |
Nonlinear autoregressive model with exogenous inputs
AIMech:NonlinearAutoregressiveExogenousModel |
Model Families | related_match | This finite-lag input-output model family provides a representation used in time-series analysis and system identification. The pinned target is an analysis task, so this is a model/task relationship rather than equivalence or an is-a assertion. |
Nonlinear autoregressive moving average model with exogenous inputs
AIMech:NonlinearAutoregressiveMovingAverageExogenousModel |
Model Families | related_match | This finite-lag input-output model family provides a representation used in time-series analysis and system identification. The pinned target is an analysis task, so this is a model/task relationship rather than equivalence or an is-a assertion. |
Pachinko allocation topics over time model
AIMech:PachinkoAllocationTopicsOverTimeModel |
Model Families | related_match | The model analyzes observed topical trends and co-occurrences over time, but is not identical to the broad time-series task or a guaranteed forecasting procedure. |
Topics over Time model
AIMech:TopicsOverTimeModel |
Model Families | related_match | The model analyzes topical prevalence associated with observed times, but its joint timestamp/content law is not the same identity as the broad time-series task. |
Wiener–Hammerstein model
AIMech:WienerHammersteinModel |
Model Families | related_match | This ordered dynamical model family represents temporal input-output relationships for system identification and analysis. The inspected AIO target is an analysis task, so the link is a model/task relationship rather than exact identity or an asserted subclass. |
Wiener model
AIMech:WienerModel |
Model Families | related_match | The source develops this ordered input-output dynamical representation for analyzing and identifying nonlinear systems from temporal observations. The pinned target denotes the analysis task; a related model/task mapping preserves that distinction and does not assert that the cascade is a task, neural layer or particular estimator. |
Import Provenance & Upstream Synchronization
Source Repo: AIO GitHub
Release Version:
aio.jsonCommit:
397851c547…License: CC-BY-4.0
Imported At: 2026-10-10T07:34:10Z
SHA256:
8f741d71408c…
Source File:
data/aio/TimeSeriesAnalysis.yaml