Σύγκριση μεθόδων

	Σύντηξη αισθητήρων	Φίλτρο Kalman
Πεδίο	Σύντηξη Δεδομένων	Χρηματοοικονομικά
Οικογένεια	Process / pipeline	Regression model
Έτος προέλευσης	2013	1989
Δημιουργός	Khaleghi, Khamis, Karray & Razavi	Harvey (structural time series treatment); Kim & Nelson (state-space with regime switching)
Τύπος	Multi-source information integration pipeline	Linear state-space model
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	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
Συναφείς	3	5
Σύνοψη	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.