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NEAREST‐NEIGHBOUR METHODS FOR TIME SERIES ANALYSIS
Author(s) -
Yakowitz S.
Publication year - 1987
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1987.tb00435.x
Subject(s) - mathematics , series (stratigraphy) , nonparametric statistics , mixing (physics) , context (archaeology) , time series , nonparametric regression , nearest neighbour , closing (real estate) , kernel (algebra) , kernel method , kernel regression , quadratic equation , statistics , econometrics , algorithm , artificial intelligence , support vector machine , computer science , combinatorics , geography , geometry , paleontology , physics , archaeology , quantum mechanics , political science , law , biology
. The nearest‐neighbour method, because of its intuitively appealing nature and competitive theoretical properties, deserves consideration in time‐series applications akin to attention it has received lately in the i.i.d. case. Here it is shown that as a nonparametric regression device, like the kernel method, under the G 2 mixing assumption, it converges in quadratic mean at the Stone‐optimal rate. In the closing sections, our methodology is extended to a broader pattern‐recognition context, and applied to hydrologic data.