Autoregressive Forecasting of Some Functional Climatic Variations

By Philippe C. Besse, Hervé Cardot, David B. Stephenson. 2000

261 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/31be9e7918fcddb20a95558df5bef5fef0c99475

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In Electrical and Electronic Engineering

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Citations in the corpus

Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule–Walker equation based approach (2011, methodology): “Some examples of application of AR modelling for climatological time series include Davies and Milionis (1994), Koscielny and Duchon (1984), Leite and Peixoto (1996) and Besse et al. (2000).”
Determining the order of the functional autoregressive model (2013, methodology): “The FAR(1) model has been applied and studied in many contexts, we list Besse et al. (2000), Damon and Guillas (2002), Antoniadis and Sapatinas (2003), Antoniadis et al. (2006), Kargin and Onatski (2008), Horváth et al. (2010) and Didericksen et al. (2012), among many others.”
Testing for stationarity of functional time series in the frequency domain (2017, background): “Antoniadis & Sapatinas (2003), Aue et al. (2015) and Besse et al. (2000) constructed prediction methodology that may find application across many areas of science, economics and finance.”
Wavelet methods for continuous-time prediction using Hilbert-valued autoregressive processes (2003, methodology): “[12], using the smoothing spline interpolation estimator (Splines), with smoothing parameter l and dimensionality ðqÞ chosen optimally by a cross-validation criterion.”
The Dynamics of Economic Functions: Modeling and Forecasting the Yield Curve (2008, background): “Previous work in the statistics literature includes the use of functional autoregressive (FAR) models for forecasting entire smooth, continuous functions by Besse and Cardot (1996) and Besse, Cardot, and Stephenson (2000).”
Curve Forecasting by Functional Autoregression (2008, methodology): “(Perhaps a switching regimes functional autoregression or a local functional autoregression, as in Besse et al (2000), would describe the whole sample data better.”
Properties of design-based functional principal components analysis (2008, methodology): “…procedures in functional spaces and have been proved useful in various domains such as chemometrics (Hastie and Mallows, 1993), economy (Kneip and Utikal, 2001), climatology (Besse et al. 2000), biology (Kirkpatrick and Heckman, 1989, Chiou et al. 2003) or remote sensing (Cardot et al., 2003).”
A Plug‐in Bandwidth Selection Procedure for Long‐Run Covariance Estimation with Stationary Functional Time Series (2016, background): “…where ‰.f /.t/ D Z 1 0 .t; s/f .s/ds; (9) and ¹ `; ` 2 Zº is a sequence of i.i.d. random elements of L2Œ0; 1 satisfying Ek 0k2 < 1; see Chapter 3 of Bosq (2000) for a rigorous mathematical development, and Besse et al. (2000) and Kargin and Onatski (2008) for some novel applications of this model.”
White Noise Testing and Model Diagnostic Checking for Functional Time Series (2016, methodology): “Various nonparametric and prediction methods for the FAR(1) models have been developed, and numerous applications have been found; see Besse et al. (2000), Laukaitis and Rac̆kauskas (2002), Antoniadis and Sapatinas (2003), Fernández de Castroa et al. (2005), Kargin and Onatski (2008), among others.”
Conditional Quantiles for Dependent Functional Data with Application to the Climatic El Niño Phenomenon (2005, methodology): “Many multivariate statistical techniques, mainly parametric in the functional model terminology, have been extended to functional data and good overviews on this topic can be found in Ramsay and Silverman (1997 and 2002) or Bosq (2000). More recently, nonparametric methods taking into account functi”
Applications of functional data analysis: A systematic review (2013, background): “Application of FDA has also been published across various scientific fields including analysis of child size evolution [9], climatic variation [4,13], handwriting in Chinese [14], acidification processes [15], land usage prediction based on satellite images [16], medical research [17-19], behavioral”
The inclusion of exogenous variables in functional autoregressive ozone forecasting (2002, methodology): “An alternative approach is to consider a local ARH predictor where local estimates of covariance and cross covariance operators are computed using kernel techniques as in Besse et al. (2000). One nonparametric way to deal with the conditional expectation ρ(x) = E [Xi |Xi−1 = x ], where (Xi) is a H-v”
Weakly dependent functional data (2010, background): “This model and its various extensions are particularly useful for prediction (see, e.g., Besse, Cardot and Stephenson [11] Damon and Guillas [23], Antoniadis and Sapatinas [4]).”
A survey of functional principal component analysis (2014, background): “2009), climatic variation forecasting (Besse et al. 2000), climatology (Meiring 2007), criminology (Berk 2008), data mining (Hand 2007), demographic forecasting (Hyndman & Ullah 2007, Hyndman & Booth 2008, Hyndman & Shang 2009), electronic commerce research (Wang, Jank & Shmueli 2008), marketing sci”
Forecasting: theory and practice (2020, methodology): “ series. Li et al. (2020c) studied the estimation of the longmemory parameter in a functional fractionally integrated time series, covering the functional unit root. From a nonparametric perspective, Besse et al. (2000) proposed a functional kernel regression method to model temporal dependence via ”