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Can ARMA models be used reliably in ecology? Les modeles ARMA peuvent-ils etre utilises avec confiance en ecologie?

, : Can ARMA models be used reliably in ecology? Les modeles ARMA peuvent-ils etre utilises avec confiance en ecologie?. Acta Oecologica. July; 184: 427-447

Several characteristics in ecological time series (limited size, non-normal distribution, missing data) are usually put forward to justify that statistical methods as ARMA modeling (AutoRegressive Moving Average) are not used unlike in economics or in hydrology. However, none study of sensitivity has been published on applying ARMA method to ecological data. We tested its robustness by simulation and considered four factors: the nature of the generating process (AR, MA or ARMA), the length of the time series, the error model and the criterion used to select a model. We studied the probability of correctly identifying the generating process in the simulation for all combinations of the four factors. This probability is high (about 908) only if time series length exceeds 50 points, if the generating process is simple (pure AR or MA) with a parameter having a high modulus (about 0.8) and if SBC is the selecting criterion; a mixed process (ARMA) was correctly identified at best in 10% to 20% of the simulations. Among the three criterions tested, AICC leads to the best selection of a model, especially with low time series length. All models are not sensitive to asymmetry of the error model in our simulations. The results are discussed according to the goals of the ecologists when analysing time series. Limitations of an automatic identification of a model are underlined.

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