Traditionally, the quality of a forecasting model is judged by how it compares, in terms of accuracy, to alternative models. However, by providing a relative measure, no indication is given as to how much scope there might be for improvements beyond the benchmark model. When judgemental methods are used alongside simple forecasting models, the scope for such improvements is considerable and dif...

The grey theory mainly works on systems analysis with poor, incomplete or uncertain messages. The popular grey model, GM(1,1) is efficient for long-term port throughput forecasting. However, it is imperfect when the throughput increases in the curve with S type or the increment of throughput is in the saturation stage. In this case, the throughput forecasting error of grey system model will bec...

Electricity price forecasting is a hypercritical issue due to the involvement of consumers and producers in electricity markets. Price forecasting plays an important role in planning and managing economic operations related with the electrical power (bidding, trading) and other decisions related with load shedding and generation rescheduling. It is also useful for optimization in electrical ene...

The Vector Autoregressive (VAR) model, the Error Correction Model (ECM), and the Kalman Filter Model (KFM) are used to forecast UK stock prices. The forecasting performance of the three models is compared using out of sample forecasting. The results show that the forecasting performance of the ECM is better than that of the VAR and the KFM, and that the VAR performs a forecasting better than th...

Exponential smoothing technique is one of the most important quantitative techniques in forecasting. The accuracy of forecasting of this technique depends on exponential smoothing constant. Choosing an appropriate value of exponential smoothing constant is very crucial to minimize the error in forecasting. This paper addresses the selection of optimal value of exponential smoothing constant to ...

in recent years, use of fuzzy collection theories for modeling of hydrological phenomenon's that is including complexity and uncertainly is considered scholars. so in this research, adaptive neuro-fuzzy inference system (anfis) is used for performance of river flow forecasting process. in this research, three parameters such as raining, temperature and daily discharge of lighvanchai basin ...

In this paper, an Adaptive-Network-based Fuzzy Inference System (ANFIS) is used for forecasting of natural gas consumption. It is clear that natural gas consumption prediction for future, surly can help Statesmen to decide more certain. There are many variables which effect on gas consumption but two variables that named Gross Domestic Product (GDP) and population, are selected as two input var...

In order to reduce the effect of numerical weather prediction (NWP) error on short term load forecasting (STLF) and improve the forecasting accuracy, a new hybrid model based on support vector regression (SVR) optimized by an artificial bee colony (ABC) algorithm (ABC-SVR) and seasonal autoregressive integrated moving average (SARIMA) model is proposed. According to the different day types and ...

Operational forecasting is hampered both by the rapid divergence of nearby initial conditions and by error in the underlying model. Interest in chaos has fuelled much work on the first of these two issues; this paper focuses on the second. A new approach to quantifying state-dependent model error, the local model drift, is derived and deployed both in examples and in operational numerical weath...