Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach

Ohtsuka, Yoshihiro; Oga, Takashi; Kakamu, Kazuhiko

doi:10.1016/j.csda.2009.06.002

Cited by 61 publications

(45 citation statements)

References 29 publications

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“…The Markov chain sampling schemes can be constructed from the full conditional distributions of β i ,

σ_{i}^{2}

, ϕ i , and θ i for i = 1, … , n and ρ . In the sampling schemes of all parameters except ρ , we use the sampling algorithm proposed by Ohtsuka et al ().…”

Section: Posterior Analysismentioning

confidence: 99%

Space‐Time Model versus VAR Model: Forecasting Electricity demand in Japan

Ohtsuka

Kakamu

2011

Journal of Forecasting

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This paper examined the forecasting performance of disaggregated data with spatial dependency and applied it to forecasting electricity demand in Japan. We compared the performance of the spatial autoregressive ARMA (SAR-ARMA) model with that of the vector autoregressive (VAR) model from a Bayesian perspective. With regard to the log marginal likelihood and log predictive density, the VAR(1) model performed better than the SAR-ARMA(1, 1) model. In the case of electricity demand in Japan, we can conclude that the VAR model with contemporaneous aggregation had better forecasting performance than the SAR-ARMA model.

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“…The Markov chain sampling schemes can be constructed from the full conditional distributions of β i ,

σ_{i}^{2}

, ϕ i , and θ i for i = 1, … , n and ρ . In the sampling schemes of all parameters except ρ , we use the sampling algorithm proposed by Ohtsuka et al ().…”

Section: Posterior Analysismentioning

confidence: 99%

Space‐Time Model versus VAR Model: Forecasting Electricity demand in Japan

Ohtsuka

Kakamu

2011

Journal of Forecasting

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“…ARMA models have been used in diverse areas of applications such as speech [2], [3], seismology [4], video [5], image [6], etc. Particularly, they have been applied in energy and meteorological prediction studies of solar radiation [7], [8], electricity demand [9], [10] and wind speed [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear model selection for PARMA processes using RJMCMC

Karakuş

Kuruoğlu

Altınkaya

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Many prediction studies using real life measurements such as wind speed, power, electricity load and rainfall utilize linear autoregressive moving average (ARMA) based models due to their simplicity and general character. However, most of the real life applications exhibit nonlinear character and modelling them with linear time series may become problematic. Among nonlinear ARMA models, polynomial ARMA (PARMA) models belong to the class of linear-in-the-parameters. In this paper, we propose a reversible jump Markov chain Monte Carlo (RJMCMC) based complete model estimation method which estimates PARMA models with all their parameters including the nonlinearity degree. The proposed method is unique in the manner of estimating the nonlinearity degree and all other model orders and model coefficients at the same time. Moreover, in this paper, RJMCMC has been examined in an anomalous way by performing transitions between linear and nonlinear model spaces.

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“…Alireza Erfani and Ahmad Jafari Samimi investigated the long memory of Stock Price Index and fitted a fractionally differenced ARMA Model to forecast out-of-sample data in [9]. Yoshihiro Ohtsuka, Takashi Oga and Kazuhiko Kakamu forecasted electricity demand in Japan with a Bayesian spatial auto-regressive ARMA approach which performs better than traditional approach in [10]. Fong-Lin Chu forecasted tourism demand with ARMA-based methods and the general impression was that the ARMA-based models perform very well in [11].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Environment Changes with the ARMA Model for Application to an Oil Tank Fire Hazard

Yuanzhi¹,

Zhang²,

Liu³

et al. 2015

Advances in Computer Science Research

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Abstract-In oil tank farm, the fire hazard causes a great loss to both people and property. Once there is a fire, to avoid more loss, a reliable, secure and practical method for putting out a fire is needed. And under this circumstance, the accurate forecasting of the changes of the environment, such as temperature or humidity, is of great value. We employed time series analysis method to forecast the temperature and humidity changes when there was a fire accident in the oil tank farm. According to the record, we modelled a suitable ARMA model. And the results show that ARMA is an effective method to predict time varying series in the oil tank farm. Our results in this paper also provide an important way of studying the fire spreading in the oil tank farm.

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Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach

Cited by 61 publications

References 29 publications

Space‐Time Model versus VAR Model: Forecasting Electricity demand in Japan

Space‐Time Model versus VAR Model: Forecasting Electricity demand in Japan

Nonlinear model selection for PARMA processes using RJMCMC

Forecasting the Environment Changes with the ARMA Model for Application to an Oil Tank Fire Hazard

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