Compara mètodes

	Fusió de sensors	Kalman Filter
Camp	Fusió de dades	Finances
Família	Process / pipeline	Regression model
Any d'origen	2013	1989
Autor original	Khaleghi, Khamis, Karray & Razavi	Harvey (structural time series treatment); Kim & Nelson (state-space with regime switching)
Tipus	Multi-source information integration pipeline	Linear state-space model
Font seminal	Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28–44.	Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737
Àlies	Multisensor Data Fusion, Multi-Sensor Integration, Information Fusion, Sensör Füzyonu	state-space model, dynamic linear model, recursive Bayesian filter, Kalman Filtresi — Finansal Durum Uzayı Modeli
Relacionats	3	5
Resum	Sensor fusion is a computational process that combines data from multiple heterogeneous sensors to produce an estimate of the environment that is more accurate, complete, and reliable than any single source alone. Systematized as a formal field by Khaleghi, Khamis, Karray, and Razavi in their 2013 state-of-the-art review in Information Fusion, the discipline addresses imperfections such as noise, incompleteness, temporal misalignment, and conflicting readings that arise whenever multiple sensing modalities operate in parallel.	The Kalman filter is a recursive algorithm that estimates financial models with time-varying parameters, hidden factors, and noisy observations inside a dynamic state-space framework. The structural time series treatment was set out by Harvey (1989), with state-space and regime-switching extensions developed by Kim and Nelson (1999); it is widely applied to pairs trading, time-varying beta estimation, and yield-curve modelling.