A New Algorithm for On-Line Multivariate ARMA Identification Using Multimodel Partitioning Theory

Pappas, Stylianos Sp.; Harkiolakis, N.; Karampelas, Panagiotis; Ekonomou, Lambros; Katsikas, Sokratis K.

doi:10.1109/pci.2008.24

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Equation (34): φ(B) T = (B) α T2

Multimodel Partition Algorithm (Mmpa)1

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Cited by 3 publications

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“…Also, in ARMA model, A (z) ~ 1 / B(z) [51]. ARMA model is moderately accurate and is applicable for short-term to medium-term forecasting [54] [55]. The accuracy can be improved by applying seasonality and calendar effects.…”

Section: Equation (34): φ(B) T = (B) α Tmentioning

confidence: 99%

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A Taxonomy of electricity demand forecasting techniques and a selection strategy

Maniatis

2017

International Journal of Management Excellence

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Section: Equation (34): φ(B) T = (B) α Tmentioning

confidence: 99%

“…The accuracy can be improved by applying seasonality and calendar effects. It is a complex method although data requirement is moderate (typically 2 years or more) [39] [54]. The relatively more popular technique is the autoregressive integrated moving average model discussed next.…”

Section: Equation (34): φ(B) T = (B) α Tmentioning

confidence: 99%

A Taxonomy of electricity demand forecasting techniques and a selection strategy

Maniatis

2017

International Journal of Management Excellence

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“…However, if the system model is not completely known the MMPA, introduced by Lainiotis [8,9], is one of the most widely used approaches for similar problems [10][11][12][13][14][15][16][17][18][19][25][26][27][28].…”

Section: Multimodel Partition Algorithm (Mmpa)mentioning

confidence: 99%

A New Hybrid Forecasting Strategy Applied to Mean Hourly Wind Speed Time Series

Pappas

Karamousantas

Chatzarakis

et al. 2014

Journal of Wind Energy

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An alternative electric power source, such as wind power, has to be both reliable and autonomous. An accurate wind speed forecasting method plays the key role in achieving the aforementioned properties and also is a valuable tool in overcoming a variety of economic and technical problems connected to wind power production. The method proposed is based on the reformulation of the problem in the standard state space form and on implementing a bank of Kalman filters (KF), each fitting an ARMA model of different order. The proposed method is to be applied to a greenhouse unit which incorporates an automatized use of renewable energy sources including wind speed power.

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