Time-Series of Distributions Forecasting in Agricultural Applications: An Intervals’ Numbers Approach
Keyword(s):
This work represents any distribution of data by an Intervals’ Number (IN), hence it represents all-order data statistics, using a “small” number of L intervals. The INs considered are induced from images of grapes that ripen. The objective is the accurate prediction of grape maturity. Based on an established algebra of INs, an optimizable IN-regressor is proposed, implementable on a neural architecture, toward predicting future INs from past INs. A recursive scheme tests the capacity of the IN-regressor to learn the physical “law” that generates the non-stationary time-series of INs. Computational experiments demonstrate comparatively the effectiveness of the proposed techniques.
1975 ◽
Vol 4
(1)
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pp. 19-32
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2021 ◽
pp. 125920
Keyword(s):
1982 ◽
Vol 3
(3)
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pp. 169-176
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2022 ◽
Vol 163
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pp. 108155