Seasonal Autoregressive Integrated Moving Average Model for Precipitation Time Series

Chang, Xinghua; Gao, Meng; Yan, Wang; Hou, Xiyong

doi:10.3844/jmssp.2012.500.505

Cited by 41 publications

(20 citation statements)

References 5 publications

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“…Generally, this step involved the analysis of the residuals as well as model comparisons (Chang et al, 2012). The Ljung-Box Q was used in the diagnostic test.…”

Section: Diagnostic Checkingmentioning

confidence: 99%

Stochastic modelling of Lake Malawi Engraulicypris sardella (Gunther, 1868) catch fluctuation

Makwinja¹,

Singini²,

Kaunda³

et al. 2018

Int. J. Fish. Aquac.

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Lake Malawi continues experiencing serious depletion of most valuable fish species. Presently, commercial and artisanal fishery are forced to target less valuable fish species. Evidently, economic importance of Engraulicypris sardella in Malawi cannot be negated as it currently contributes over 70% of the total annual landings. However, such highest contribution could be a sign of harvesting pressure. Therefore, as the species continues being increasingly exploited, the development of scientific understanding through application of stochastic models is particularly relevant for present and future policy making and formulation of strategies to sustain the resource in the lake. Thus, the study was designed to forecast the annual catch trend of E. sardella from Lake Malawi.

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“…Generally, this step involved the analysis of the residuals as well as model comparisons (Chang et al, 2012). The Ljung-Box Q was used in the diagnostic test.…”

Section: Diagnostic Checkingmentioning

confidence: 99%

Stochastic modelling of Lake Malawi Engraulicypris sardella (Gunther, 1868) catch fluctuation

Makwinja¹,

Singini²,

Kaunda³

et al. 2018

Int. J. Fish. Aquac.

View full text Add to dashboard Cite

show abstract

“…For example, Wagner (2010) compared seasonal ARIMA model and vector time series model for forecasting daily demand in cash supply chains. Chang et al (2012) forecasted monthly precipitation in Yantai, China with seasonal ARIMA model. The results of these studies show that this model performed well and gives less errors compared with other models over short time periods.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Forecasting the Algiers Stock Exchange Returns using the Box-Jenkins Methodology<strong> </strong>

Ms¹

2019

Preprint

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The Algiers Stock Exchange (ASE) is the only stock exchange in Algeria. It’s one of the newest and smallest emerging stock exchanges in the world. The focus of this paper is to model and forecast monthly returns of the ASE index (DZAIRINDEX) using The Box- Jenkins methodology. The period of this study is from Jun 2010 to July 2019. According to Akaike’s Information Criterion (AIC) estimator, the Seasonal Autoregressive Integrated Moving Average SARIMA(2,0,0)(0,0,1) is chosen as the best model for forecasting the monthly DZAIRINDEX returns. Diagnostic tests confirm that the fitted model is adequate, where the residuals of this model are normally distributed with no autocorrelation and no heteroskedasticity. The forecast of the monthly DZAIRINDEX returns for one year ahead using this model shows a decreasing fluctuations trend. Based on different measures of forecast accuracy such as ME, MAE, RMSE, MASE, we show that the forecast accuracy of SARIMA(2,0,0)(0,0,1) is acceptable and this model performs much better than a naïve model. These results could be used by the financial communities in Algeria to deal with stock exchange risks and to improve their decisions.

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“…The autoregressive integrated moving average model is a well-known time series prediction method first proposed by Box and Jenkins in the early 1970s [4]. It transforms a nonstationary time series into a stationary time series, and then uses the lag value of the dependent variable and the present value and the lag value of the random error term as the independent variables to construct the regression model [5]. The grey model can make fuzzy long-term description of the law of things development through establishing the grey differential prediction model with a small amount of incomplete information [6].…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Power Grid Investment in China Based on Support Vector Machine Optimized by Differential Evolution Algorithm and Grey Wolf Optimization Algorithm

2018

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Abstract:In recent years, the construction of China's power grid has experienced rapid development, and its scale has leaped into the first place in the world. Accurate and effective prediction of power grid investment can not only help pool funds and rationally arrange investment in power grid construction, but also reduce capital costs and economic risks, which plays a crucial role in promoting power grid investment planning and construction process. In order to forecast the power grid investment of China accurately, firstly on the basis of analyzing the influencing factors of power grid investment, the influencing factors system for China's power grid investment forecasting is constructed in this article. The method of grey relational analysis is used for screening the main influencing factors as the prediction model input. Then, a novel power grid investment prediction model based on DE-GWO-SVM (support vector machine optimized by differential evolution and grey wolf optimization) algorithm is proposed. Next, two cases are taken for empirical analysis to prove that the DE-GWO-SVM model has strong generalization capacity and has achieved a good prediction effect for power grid investment forecasting in China. Finally, the DE-GWO-SVM model is adopted to forecast power grid investment in China from 2018 to 2022.

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Seasonal Autoregressive Integrated Moving Average Model for Precipitation Time Series

Cited by 41 publications

References 5 publications

Stochastic modelling of Lake Malawi Engraulicypris sardella (Gunther, 1868) catch fluctuation

Stochastic modelling of Lake Malawi Engraulicypris sardella (Gunther, 1868) catch fluctuation

Modeling and Forecasting the Algiers Stock Exchange Returns using the Box-Jenkins Methodology<strong> </strong>

Forecasting of Power Grid Investment in China Based on Support Vector Machine Optimized by Differential Evolution Algorithm and Grey Wolf Optimization Algorithm

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