جستجوی مقالات مرتبط با کلیدواژه « time series forecasting » در نشریات گروه « صنایع »

Coronavirus (COVID-19) is a pandemic that has affected all countries of the world. Forecasting the spread of corona disease will lead to the necessary measures to be taken by the authorities to control this disease. These include increasing vaccinations, quarantining cities and banning entry and exit, increasing the capacity of hospital beds, setting up round-the-clock vaccination centers, requiring the use of masks in public places, and observing social distances. Therefore, predicting such cases will reduce the number of corona cases and therefore reduce the mortality rate.

In this paper, using the Singular Spectrum Analysis (SSA) algorithm, the sixth peak of coronavirus in Iran is predicted by considering the current situation. To improve the grouping process of the SSA algorithm, eigenvalues have been selected in the optimization process, so that the predicted time series of which has been significantly improved according to the error-index.

Comparing the proposed method with other forecasting methods include Autoregressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), TBATS, and Neural Network Autoregression (NNAR), it is observed that the forecasting error is acceptable and the SSA method can be used for forecasting.

Originality/Value: 

This article predicts a new case of COVID-19 using efficient method SSA and the presented results confirm the effectiveness of the proposed method.

Forecasting methods are one of the most efficient available approaches to make managerial decisions in various fields of science. Forecasting is a powerful approach in the planning process, policy choices and economic performance. The accuracy of forecasting is an important factor affects the quality of the decisions that generally has direct and non-strict relationship with the quality of decisions. This is the most important reason that why endeavor for improving the forecasting accuracy has never been stopped in the literature. Electricity demand forecasting is one of the most challenging areas forecasting and important factors in the management of energy systems and economic performance. Determining the level of electricity demand is essential for careful planning and implementation of the necessary policies. For this reason electricity demand forecasting is important for financial and operational managers of electricity distribution. The unique feature of the electricity which makes it more difficult forecasting in comparison with other commodity is the impossibility of storing it in order to use in the future. In other words, the production and consumption of electricity should be taken simultaneously. It has caused to create a high level of complexity and ambiguity in electricity markets data. Computational intelligence and soft computing approaches are among the most precise and useful methods for modeling the complexity and uncertainty in data. In this paper a soft intelligent method by combining mentioned methods is proposed in order to electricity demand forecasting. The main idea of the proposed model is to simultaneously use advantages of these models in modeling complex and ambiguous systems. Empirical results indicate that proposed model can achieve more accurate results rather than its component (Seasonal auto-regressive Integrated Moving Average models, artificial neural network) and also other current single forecasting methods such as classic regression, Seasonal Auto-Regressive Integrated Moving Average-fuzzy models and support vector machine

Decision-making as one of the principles of management is considered an important factor in prosperity of the organizations. This is so important that managers use efficient tools to improve the quality of their decisions. Steel industry is one of the major industries in this country; consequently, it deserves special attention. In this paper, the main aim is to use scientific methods to manage crude steel consumption in the country. However, the literature shows that it is relatively difficult to yield accurate results in the prediction of consumption, especially in long-term horizon. Researchers believe that high level of complexity and uncertainty in financial markets is main reason of this matter. Therefore, in this paper, a hybrid of intelligent and soft computing models have been used as an effective way in order to model the complexities and uncertainties simultaneously in the data. In this way, the list of variables is recognized based on the literature and expert opinions. Then the linear and nonlinear relationships and also correlations between variables are evaluated and final explanatory variables specified. Finally, four models including hard classic, soft classic, hard intelligent and soft intelligent are designed to predict steel consumption in both short and long term horizons and their results are compared with each other. Empirical results indicate that using the hard intelligent model makes improvement 22.68% and 41.41% in comparison with hard classic model in short and long term horizons respectively in Root Mean Squared Error (RMSE). In addition, the soft intelligent model makes improvement 43.01% and 92.72% in comparison with soft classic model and hard classic model respectively in short term horizon and 34.68% and 91.53% in long term horizon. Results of the study indicate superiority of the soft intelligent models over hard intelligent models and superiority of hard intelligent models over hard classic models. Results of the study indicate superiority of the soft intelligent models and hard intelligent models over hard intelligent models and hard classic models respectively.