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Stochastic Temporal Data Upscaling Using the Generalized k-Nearest Neighbor Algorithm

John Mashford

International Journal of Stochastic Analysis2018https://doi.org/10.1155/2018/2487947article
ABDC B
Weight
0.26

Abstract

Three methods of temporal data upscaling, which may collectively be called the generalized k-nearest neighbor (GkNN) method, are considered. The accuracy of the GkNN simulation of month by month yield is considered (where the term yield denotes the dependent variable). The notion of an eventually well-distributed time series is introduced and on the basis of this assumption some properties of the average annual yield and its variance for a GkNN simulation are computed. The total yield over a planning period is determined and a general framework for considering the GkNN algorithm based on the notion of stochastically dependent time series is described and it is shown that for a sufficiently large training set the GkNN simulation has the same statistical properties as the training data. An example of the application of the methodology is given in the problem of simulating yield of a rainwater tank given monthly climatic data.

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https://doi.org/https://doi.org/10.1155/2018/2487947

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@article{john2018,
  title        = {{Stochastic Temporal Data Upscaling Using the Generalized k-Nearest Neighbor Algorithm}},
  author       = {John Mashford},
  journal      = {International Journal of Stochastic Analysis},
  year         = {2018},
  doi          = {https://doi.org/https://doi.org/10.1155/2018/2487947},
}

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Evidence weight

0.26

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.20 × 0.15 = 0.03
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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