Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir

Valipour, Mohammad; Banihabib, Mohammad Ebrahim; Behbahani, S. M. R.

doi:10.1016/j.jhydrol.2012.11.017

Cited by 757 publications

(282 citation statements)

References 12 publications

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“…The season item of seawall settlement time series is nonstationary and not oscillated at a fixed value [16]. Although it is not a random sequence, some relevant information of the nonstationary time series can still be extracted, but the prediction accuracy by the autoregressive moving average (ARMA) model is low in this case.…”

Section: Fitting the Seasonmentioning

confidence: 99%

“…The ARIMA model orders and are initially identified based on the truncation and tailing of both the autocorrelation and partial autocorrelation coefficients of the stationary sequence. Then the values of and are determined by means of the order criterion [16], and the Akaike information criterion (AIC) was used in this study.…”

Section: Fitting the Seasonmentioning

confidence: 99%

“…The model is tested after the parameter estimation, which is to test whether { } is smooth white noise. The model establishment will be completed if the test is passed; otherwise the model parameters must be reestimated [16].…”

Section: Fitting the Seasonmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Seawall Settlement Based on a Combined LS‐ARIMA Model

Qin

Chen

2017

Mathematical Problems in Engineering

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The analysis and prediction of seawall settlement are important for seawall engineering maintenance and disaster prevention. Based on the measured seawall settlement time series data, a combined LS-ARIMA forecasting model that fits the trend item by the leastsquare (LS) method and the season item by the differential self-regression moving average (ARIMA) model was proposed in this study. The monitoring data of one seawall project in Zhejiang, China, is taken as an example to verify the model efficiency and prediction ability. The results show that the prediction accuracy of the new combined LS-ARIMA model was high, with the average relative error (ARE) of 0.23%, much better than that of the traditional ARIMA model (ARE = 0.70%) and the GM (1, 1) model (ARE = 33.43%). This new model has clear physical conception and can effectively improve the prediction accuracy, implying that it can fully tap the dynamic information of monitoring data. The proposed model in this study provides a new research idea for data analysis and prediction of the seawall settlement.

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Section: Fitting the Seasonmentioning

confidence: 99%

Section: Fitting the Seasonmentioning

confidence: 99%

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Prediction of Seawall Settlement Based on a Combined LS‐ARIMA Model

Qin

Chen

2017

Mathematical Problems in Engineering

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“…Than, the prediction were repeated five times for each sub-trace. The To determine the order of the models, we use Akaike's Information Criterion (AIC), presented, among others, in [59] in section 8.6, and defined as…”

Section: Traffic Prediction With Arima and Farima Modelsmentioning

confidence: 99%

Analysis and modelling of traffic produced by adaptive HTTP-based video

Biernacki

2016

Multimed Tools Appl

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The increase of HTTP-based video popularity causes that broadband and Internet service providers' links transmit mainly multimedia content. Network planning, traffic engineering or congestion control requires an understanding of the statistical properties of network traffic; therefore, it is desirable to investigate the characteristic of traffic traces generated by systems which employ adaptive bit-rate streaming. Our first contribution is an investigation of traffic originating from 120 client-server pairs, situated in an emulated content distribution network, and multiplexed onto a single network link. We show that the structure of the traffic is distinct from the structure generated by the first and second generation of HTTP video systems, and furthermore, not similar to the structure of general Internet traffic. The obtained traffic exhibits negative and positive correlations, anti-persistence, and its distribution function is skewed to the right. Our second contribution is an approximation of the traffic by ARIMA/FARIMA processes blue and artificial neural networks. As we show, the obtained traffic models are able to enhance the performance of an adaptive streaming algorithm.

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“…The infragravity-wave heights and periods were estimated from low-pass ltered data using formula (3) and (4). Data were proceed by Matlab soft [32][33][34].…”

Section: Wave Data Processingmentioning

confidence: 99%

Relationship between high-frequency sediment-level oscillations in the swash zone and inner surf zone wave characteristics under calm wave conditions

Liu

2016

Open Geosciences

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Swash zone topography rapidly responds to the surf zone waves. Understanding how sandy beaches respond to wave action is critical for beach erosion research, and plays a critical role in the design and maintenance of shore protection structures. The main objectives of this study were to detect the relationship between high-frequency beachface oscillations and surf zone wave characteristics under plunging breakers by using Canonical Correlation Analysis (CCA). The study site is located in Houjiangwan Bay, eastern Guangdong. Topography data were sampled at 6 min intervals. The wave characteristic parameters were calculated by spectrum method. During the eld work, the beach showed a re ective state and plunging breakers controlled the surf zone. The beach cusp topography was destructed gradually. The analysis provides 4 canonical correlation processes between the beachface variations and surf zone waves, which explained 95.28% of the overall variation in the data. The result shows wave steepness, the irregularity factor and spectral broadness factor had strong impacts on the topography. The wave steepness was the most important factor for beach pro le variations. The results of the present study indicate that data-driven statistical analysis, such as CCA, is useful for analyzing pro le response to waves if there is strong correlation between the two variables (beach pro les and wave).

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Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir

Cited by 757 publications

References 12 publications

Prediction of Seawall Settlement Based on a Combined LS‐ARIMA Model

Prediction of Seawall Settlement Based on a Combined LS‐ARIMA Model

Analysis and modelling of traffic produced by adaptive HTTP-based video

Relationship between high-frequency sediment-level oscillations in the swash zone and inner surf zone wave characteristics under calm wave conditions

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