Next Article in Journal
On the Introduction of Diffusion Uncertainty in Telecommunications’ Market Forecasting
Previous Article in Journal
Analyzing the Effectiveness of COVID-19 Lockdown Policies Using the Time-Dependent Reproduction Number and the Regression Discontinuity Framework: Comparison between Countries
Proceeding Paper

Time-Series of Distributions Forecasting in Agricultural Applications: An Intervals’ Numbers Approach

HUMAIN-Lab, Department of Computer Science, International Hellenic University (IHU), 65404 Kavala, Greece
*
Author to whom correspondence should be addressed.
Presented at the 7th International conference on Time Series and Forecasting, Gran Canaria, Spain, 19–21 July 2021.
Academic Editors: Ignacio Rojas, Fernando Rojas, Luis Javier Herrera and Hector Pomare
Eng. Proc. 2021, 5(1), 12; https://doi.org/10.3390/engproc2021005012
Published: 25 June 2021
(This article belongs to the Proceedings of The 7th International conference on Time Series and Forecasting)
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. View Full-Text
Keywords: agriculture 4.0; big data; computational intelligence; Intervals’ Number (IN); non-stationary; prediction regressor model; time-series agriculture 4.0; big data; computational intelligence; Intervals’ Number (IN); non-stationary; prediction regressor model; time-series
Show Figures

Figure 1

MDPI and ACS Style

Bazinas, C.; Vrochidou, E.; Lytridis, C.; Kaburlasos, V.G. Time-Series of Distributions Forecasting in Agricultural Applications: An Intervals’ Numbers Approach. Eng. Proc. 2021, 5, 12. https://doi.org/10.3390/engproc2021005012

AMA Style

Bazinas C, Vrochidou E, Lytridis C, Kaburlasos VG. Time-Series of Distributions Forecasting in Agricultural Applications: An Intervals’ Numbers Approach. Engineering Proceedings. 2021; 5(1):12. https://doi.org/10.3390/engproc2021005012

Chicago/Turabian Style

Bazinas, Christos, Eleni Vrochidou, Chris Lytridis, and Vassilis G. Kaburlasos 2021. "Time-Series of Distributions Forecasting in Agricultural Applications: An Intervals’ Numbers Approach" Engineering Proceedings 5, no. 1: 12. https://doi.org/10.3390/engproc2021005012

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop