A new regression model for bimodal data and applications in agriculture

Vasconcelos, Julio Cezar Souza; Cordeiro, Gauss M.; Ortega, Edwin M. M.; Rezende, Édila Maria de

doi:10.1080/02664763.2020.1723503

Cited by 9 publications

(1 citation statement)

References 20 publications

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“…As a rule, time series of empirical data have a complex non-stationary structure and contain local features of various forms. The methods for the time series analysis include deterministic [11], stochastic [12][13][14] approaches and their various combinations [15][16][17][18][19]. Traditional methods for data time series modeling and analysis (AR models, ARMA [20,21], exponential smoothing [22], stochastic approximation [13], etc.)…”

Section: Introductionmentioning

confidence: 99%

Hybrid Model for Time Series of Complex Structure with ARIMA Components

2021

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A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

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