Modeling and Prediction of Great Salt Lake Elevation Time Series Based on ARFIMA

Volume 9: 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications

2017

Self Cite

Strong coupling between values at different time that exhibit properties of long range dependence, non-stationary, spiky signals cannot be processed by the conventional time series analysis. The ARFIMA model, which employs the fractional order signal processing techniques, is the generalization of the conventional integer order models — ARIMA and ARMA model. Therefore, it has much wider applications since it could capture both short-range dependence and long range dependence. For now, several software have developed functions dealing with ARFIMA processes. However, it could be a big difference, if using different numerical tools for time series analysis. Time to time, being asked about which tool is suitable for a specific application, the authors decide to carry out this survey to present recapitulative information of the available tools in the literature, in hope of benefiting researchers with different academic backgrounds. In this paper, 4 primary functions concerning simulation, fractional order difference filter, estimation and forecast are compared and evaluated respectively in the different software and informative comments are also provided for selection.

Section: Detc2017-67483mentioning

confidence: 99%

An Evaluation of ARFIMA Programs

Volume 9: 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications

2017

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“…In the [73], the author highlights the necessity of long-memory models characterized also by a strong flexibility in modeling the low-lags autocorrelations by introducing ARFIMA models which represented a potentially powerful tool for modeling stationary hydrological records. Sun et al applied the ARFIMA model to analyze the data and predict the future levels of the elevation of Great Salt Lake (GSL) [74]. The results showed that the prediction results have a better performance compared to the conventional ARMA models.…”

Section: Solar Energymentioning

confidence: 99%

Energy Informatics and Fractional Calculus

Volume 9: 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications

2017

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Energy informatics (EI) is relatively a blossoming and dynamic research area especially in today’s green manufacturing world. Using renewable energy and clean technology are the keys to a revitalization of the world manufacturing and job creation. Green manufacturing, which reduces resource use, waste and emissions and saving the energy, has become the priority for the manufacturers. Therefore, EI has come into a desirable solution. The fractional calculus (FC) is a mighty tool which can characterize the complex properties of the natural and social phenomena. In this paper, we have provided an overview on the current EI by describing current research topics and methods and then pointed out an outlook of how this new field might be evolved with FC in the coming future.

“…Compared to the conventional integer order models, the ARFIMA model gives a better fit and result when dealing with the data which possess the LRD property. Sun et al applied the ARFIMA model to analyze the data and predict the future levels of the elevation of Great Salt Lake (GSL) [7]. The results showed that the prediction results have a better performance compared to the conventional ARMA models.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of ARFIMA (Autoregressive Fractional Integral Moving Average) Programs

2017

Axioms

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Strong coupling between values at different times that exhibit properties of long range dependence, non-stationary, spiky signals cannot be processed by the conventional time series analysis. The autoregressive fractional integral moving average (ARFIMA) model, a fractional order signal processing technique, is the generalization of the conventional integer order models-autoregressive integral moving average (ARIMA) and autoregressive moving average (ARMA) model. Therefore, it has much wider applications since it could capture both short-range dependence and long range dependence. For now, several software programs have been developed to deal with ARFIMA processes. However, it is unfortunate to see that using different numerical tools for time series analysis usually gives quite different and sometimes radically different results. Users are often puzzled about which tool is suitable for a specific application. We performed a comprehensive survey and evaluation of available ARFIMA tools in the literature in the hope of benefiting researchers with different academic backgrounds. In this paper, four aspects of ARFIMA programs concerning simulation, fractional order difference filter, estimation and forecast are compared and evaluated, respectively, in various software platforms. Our informative comments can serve as useful selection guidelines.