RT Journal Article
T1 Deterministic Chaos Detection and Simplicial Local Predictions Applied to Strawberry Production Time Series
A1 Borrero Sánchez, Juan Diego
A1 Mariscal, Jesús
AB In this work, we attempted to find a non-linear dependency in the time series of strawberryproduction in Huelva (Spain) using a procedure based on metric tests measuring chaos. This studyaims to develop a novel method for yield prediction. To do this, we study the system’s sensitivityto initial conditions (exponential growth of the errors) using the maximal Lyapunov exponent. Tocheck the soundness of its computation on non-stationary and not excessively long time series, weemployed the method of over-embedding, apart from repeating the computation with parts of thetransformed time series. We determine the existence of deterministic chaos, and we conclude thatnon-linear techniques from chaos theory are better suited to describe the data than linear techniquessuch as the ARIMA (autoregressive integrated moving average) or SARIMA (seasonal autoregressivemoving average) models. We proceed to predict short-term strawberry production using Lorenz’sAnalog Method
PB MDPI
SN 2227-7390 (electrónico)
YR 2021
FD 2021
LK http://hdl.handle.net/10272/20294
UL http://hdl.handle.net/10272/20294
LA eng
NO This research was funded by Junta de Andalucía. Consejería de la Presidencia, AdministraciónPública e Interior. Secretaría General de Acción Exterior grant number G/82A/44103/00 01
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026