It is shown that regression-based methods of forecast combination lead to serially correlated combined prediction errors. The form of the serial correlation is characterized, and specification, estimation, and prediction are treated. A fully optimal combined predictor, which exploits the serial correlation, is developed and compared with existing regression-based methods in a numerical example,...

FORECASTING OF CAPITALIZATION OF BANKING INSTITUTIONS IN UKRAINE Abstract. In this article peculiarities of existing approaches to the assessment of the bank capitalization are investigated. The advancing tools for mathematical analysis modelling were put forward by authors for predicting of the Ukrainian banks capitalization. The forecast of Ukrainian bank capitalization is given for various c...

Energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. However, this forecasting problem is complex due to nonlinear, non-stationary, and time variant behavior of electricity price time series. Accordingly, in this paper a new strategy is proposed for electricity price forecast. The forecast strategy includes Wavelet Transform (WT...

precipitation is one of important parameters of climatology and atmospheric science that have more importance in human life. recently, extensive flood and drought entered many damage to most parts of the world. precipitation forecasting and alerts management role is responsible for these problems. today, artificial neural networks are one of developed method that applied for estimate and predic...

the purpose of this research is to investigate the relationship between earnings variabilityand earnings forecast using neural networks in companies listed on tehran stock exchange. theresearch is of the library and analytical-causal study type, and is based on panel data analysis andneural networks. in this research, financial information of 98 companies from 19 industries intehran stock excha...

Whether listed companies run soundly or not has direct impact on development of capital market, therefore, how to forecast financial crisis of listed companies accurately has been a widespread topic. Essentially financial crisis of listed companies is mainly about model pattern classification. Considering that Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) have great perform...

A forecasting methodology for prediction of both normal prices and price spikes in the day-ahead energy market is proposed. The method is based on an iterative strategy implemented as a combination of two modules separately applied for normal price and price spike predictions. The normal price module is a mixture of wavelet transform, linear AutoRegressive Integrated Moving Average (ARIMA) and ...

For detecting anomalies or interventions in the field of forest monitoring we propose an approach based on the spatial and temporal forecast of satellite time series data. For each pixel of the satellite image three different types of forecasts are provided, namely spatial, temporal and combined spatio-temporal forecast. Spatial forecast means that a clustering algorithm is used to group the ti...

Water quality prediction can be applied to guard against all kinds of emergency events and provide decision support to the relevant departments. Many water quality prediction methods have supplied, such as time series analysis method, fuzzy algorithm, artificial neural network, wavelet analysis. Time series analysis method is suitable for the changing apparent data sequence, the predicted data ...