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Knowledge Discovery from Data Streams
book

Knowledge Discovery from Data Streams

by Joao Gama
May 2010
Intermediate to advanced content levelIntermediate to advanced
255 pages
8h 11m
English
Chapman and Hall/CRC
Content preview from Knowledge Discovery from Data Streams
Time Series Data Streams 177
filter is widely used in engineering for two main purposes: for combining mea-
surements of the same variables but from different sensors, and for combining
an inexact forecast of a system’s state with an inexact measurement of the
state. We use a Kalman filter to combine the neural network forecast with
the observed value at time t k, where k depends on the horizon forecast
as defined above. The one-dimensional Kalman filter works by considering:
ˆy
i
= ˆy
i1
+ K(y
i
ˆy
i1
) where σ
2
i
= (1 K)σ
2
i1
and K =
σ
2
i1
σ
2
i1
+σ
2
r
.
11.3 Similarity between Time-Series
Most of time-series analysis techniques (clustering, classification, novelt ...
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Publisher Resources

ISBN: 9781439826126