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Autoregressive model

Known as: Autoregressive, AR process, Stochastic term 
In statistics and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it describes certain time… Expand
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2019
Review
2019
The price volatility of cryptocurrencies is often cited as a major hindrance to their wide-scale adoption. Consequently, during… Expand
Highly Cited
2019
Highly Cited
2019
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better… Expand
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Review
2017
Review
2017
A thorough review has been performed on interpolation methods to fill gaps in time-series, efficiency criteria, and uncertainty… Expand
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Highly Cited
2002
Highly Cited
2002
Recognition and analysis of spatial autocorrelation has defined a new par- adigm in ecology. Attention to spatial pattern can… Expand
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Highly Cited
1998
Highly Cited
1998
This paper proposes a new statistical model for the analysis of data which arrive at irregular intervals. The model treats the… Expand
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Highly Cited
1996
Highly Cited
1996
Abstract The new class of Fractionally Integrated Generalized AutoRegressive Conditionally Heteroskedastic (FIGARCH) processes is… Expand
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Highly Cited
1992
Highly Cited
1992
Abstract We present a multiresolution simultaneous autoregressive (MR-SAR) model for texture classification and segmentation… Expand
Highly Cited
1991
Highly Cited
1991
This paper contains the likelihood analysis of vector autoregressive models allowing for cointegration. The author derives the… Expand
Highly Cited
1979
Highly Cited
1979
Abstract Let n observations Y 1, Y 2, ···, Y n be generated by the model Y t = pY t−1 + e t , where Y 0 is a fixed constant and… Expand
Highly Cited
1970
Highly Cited
1970
Many statistical models, and in particular autoregressive-moving average time series models, can be regarded as means of… Expand
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