Time series analysis applied to construct US natural gas price functions for groups of states

Kalashnikov, Vyacheslav V.; Matis, Timothy I.; Pérez-Valdés, Gerardo A.

doi:10.1016/j.eneco.2009.11.006

Cited by 13 publications

(7 citation statements)

References 15 publications

(17 reference statements)

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“…In this section, a combination of both grouping methods mentioned in [26] into a GRASP heuristic is proposed. The resulting technique inherits the replicative property of the dendrogram method, while retaining the statistical significance of the heuristic algorithm.…”

Section: Dendrogram-grasp Grouping Methods (Dggm)mentioning

confidence: 99%

“…As explained in our previous work [26], a carefully designed regression function can help achieve such strong assumptions. Nevertheless, the study of such relationships and the possibility of forming state clusters based merely upon time series data analysis turned out to be interesting by itself, and we developed two different approaches to partition the collection of states.…”

Section: Former and Current Approachesmentioning

confidence: 99%

“…Our previous paper [26] aimed at a very definite objective regarding the qualities of the regression model: it had to correlate consumption and price of residential natural gas series, using the former as the explicative variable because of the ease in its forecasting. The expression thus obtained served its purpose well, as demonstrated in its application to the optimization models in [27]; nevertheless, a more inclusive approach would involve series that comprise more information.…”

Section: Former and Current Approachesmentioning

confidence: 99%

“…Previous experiments [26] have shown that what is called the "average euclidean" metric [28] delivers satisfactorily high 2 levels with a better homogeneity in the resulting groups than other linkage function options.…”

Section: Dendrogramsmentioning

confidence: 99%

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US Natural Gas Market Classification Using Pooled Regression

Kalashnikov

Pérez-Valdés

Matis

et al. 2014

Mathematical Problems in Engineering

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Natural gas marketing has considerably evolved since the early 1990s, when a set of liberalizing rules were passed in both the United States and the European Union that eliminated state-driven regulations in favor of open energy markets. These new rules changed many things in the business of energetics, and therefore new research opportunities arose. Econometric studies about natural gas emerged as an important area of study since natural gas may now be sold and traded in a number of stock markets, each one responding to potentially different behavioral drives. In this work, we present a method to differentiate sets of time series based on a regression model relating price, consumption, supply, and other factors. Our objective is to develop a method to classify different areas, regions, or states into groups or classes that share similar regression parameters. Once obtained, these groups may be used to make assumptions about corresponding natural gas prices in further studies.

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Section: Dendrogram-grasp Grouping Methods (Dggm)mentioning

confidence: 99%

Section: Former and Current Approachesmentioning

confidence: 99%

Section: Former and Current Approachesmentioning

confidence: 99%

Section: Dendrogramsmentioning

confidence: 99%

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US Natural Gas Market Classification Using Pooled Regression

Kalashnikov

Pérez-Valdés

Matis

et al. 2014

Mathematical Problems in Engineering

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“…To take seasonal and periodic factors into account, time series models are adopted to predict the demand for CBM. Time series (Kalashnikov et al., 2010; Kan et al., 2020) can reflect the trend of one or some random variables changing over time. The core of time series prediction is to find the law from the data and use it to make predictions about future data.…”

Section: Introductionmentioning

confidence: 99%

Research on forecasting coal bed methane demand and resource allocation system based on time series

Linghu

Chen

Yan

2020

Energy Exploration & Exploitation

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The mining area is the main place for the development and utilization of Coalbed Methane (CBM), and there are a series of systems for the development and utilization of CBM. However, owing to lack of a clear understanding of demand-side gas consumption rules and a reasonable resource allocation system, a large amount of CBM resources in the mining area are wasted. In order to predict the demand for CBM dynamically, the Seasonal Auto Regressive Integrated Moving Average (SARIMA) model, Additive Holt-Winters (AHW) model and Multiplicative Holt-Winters (MHW) model based on time series are used to predict the monthly demand for CBM in Yangquan Mine Area in 2020, respectively. Then the predicted results are evaluated by using the prediction model parameters combined with the characteristics of actual demand for CBM. Finally, a resource allocation system under different supply and demand conditions is built to reduce the waste of resources. In this paper, it is found that the information of the actual data is not sufficiently extracted in the MHW model while the SARIMA model can reflect the cyclical trend of monthly demand for CBM under ideal conditions. Furthermore, the AHW model can reasonably predict the demand for CBM under the influence of COVID-19, with a mean relative error of 0.099. The supply and demand distribution system built based on the proposed models can solve the problem of seasonal unevenness of CBM demand in mining areas and ensure the economic benefits of mining areas.

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