This paper presents a study of neural network forecasting construction system. Forecasting, especially financial forecasting has been attracting researchers and practitioners for many years. Many experiments suggest that neural networks can outperform conventional models in most cases. However, due to the tedious and time consuming of neural networks training, some of models reported in literat...

To take into account different character of distinct segments of non-stationary financial time series the multi-agent system based forecasting algorithm is suggested. The primary goal of present paper is to introduce methodological findings that could help to reduce one step ahead forecasting error. In contrast to previous investigation [6], instead of single prediction rule we use a system of ...

abstract one of the major factors on the amount of water resources is river flow which is so dependent to the hydrologic and meteorologic phenomena. simulation and forecasting of river flow makes the decision maker capable to effectively manage the water resources projects. so, simulation and forecasting models such as artificial neural networks (anns) are commonly used for simulation and predi...

Photovoltaic power generation forecasting is an important task in renewable energy power system planning and operating. This paper explores the application of neural networks (NN) to study the design of photovoltaic power generation forecasting systems for one week ahead using weather databases include the global irradiance, and temperature of Ghardaia city (south of Algeria) using a data acqui...

Load forecasting is important for safe and cost-effective operation of recent power utilities. It helps in taking many decisions regarding energy purchasing and generation, maintenance, etc. Further, load forecasting provides information which is able to be used for energy interchange with other utilities. Over the years, a number of methods have been proposed for load forecasting. This paper f...

in this paper, forecasting error for state tax revenues is assessed. for this purpose, using the regression equation, statistical index, and mean percent error, root mean square error, mean absolute percent error and error analysis of tile inequality coefficients are applied on anticipated revenue from taxes on legal entities, income taxes, wealth tax, taxes on imports and taxes on consumption ...

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...

When committed to forecasting or classification tasks Neural Networks (NNs) are typically trained with respect to Euclidean distance minimisation / likelihood maximisation. This is commonly irrespective of any other end user preferences. In a number of situations, most notably time series forecasting, users may have other objectives than solely Euclidean minimisation. Users may, for example, de...

In the present age of globalization, technology-revolution and sustainable development, the presence of seasonality in tourist arrivals is considered as a key policy issue that affects the global tourism industry by creating instability in the demand and revenues. The seasonal component in a time-series distorts the prediction attempts for policy-making. In this context, it is quintessential to...

The scientific evaluation methodology for the forecast accuracy of wind power forecasting models is an important issue in the domain of wind power forecasting. However, traditional forecast evaluation criteria, such as Mean Squared Error (MSE) and Mean Absolute Error (MAE), have limitations in application to some degree. In this paper, a modern evaluation criterion, the Diebold-Mariano (DM) tes...