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Financial forecasting is the basis for budgeting activities and estimating future financing needs. Applying machine learning and data mining models to financial forecasting is both effective and efficient. Among different kinds of machine learning models, kernel methods are well accepted since they are more robust and accurate than traditional models, such as neural networks. However, learning from...
In wastewater treatment plants, It's difficult to acquire online data of BOD5 (Biochemical Oxygen Demand for 5 days) due to its characteristic and unreliability of on-line sensors. Furthermore, although soft sensors models are widely used in wastewater treatment, only a few approaches for soft sensors models are designed to address the problems currently existing in the wastewater treatment. In such...
The work of tax revenue forecast is very important. Current revenue forecast system in our country has just begun, so compared with foreign country, there is a larger gap. Theutility of tax forecast system is still relatively low-level for decision-making reference. In this paper, we propose an intelligent forecast system. By means of advanced machin elearning methods, via establishing of more scientific...
The volatility of crude oil market and its chain effects to the world economy augmented the interest and fear of individuals, public and private sectors. Previous statistical and econometric techniques used for prediction, offer good results when dealing with linear data. Nevertheless, crude oil price series deal with high nonlinearity and irregular events. The continuous usage of statistical and...
Wind power prediction is important to the operation of power system with comparatively large mount of wind power. It can relieve or avoid the disadvantageous impact of wind farm on power systems. Because the traditional neural network may fall into local convergence, so it will be effective to improve the training algorithm to improve its convergence and accuracy of prediction. In this paper, a model...
This paper applies DEA model to a sample of 58 power plate listed companies in the securities market in China in 2008, with a view to identifying the financial risk companies and non-financial risk companies, instead of using ST in the past. Then, after comparing logit regression model and neural network LVQ in predicting the company financial risks, the conclusion was drawn that neural network LVQ...
Aiming at the characters of day-ahead electricity price, an electricity price prediction model is established by combining the intelligent modeling methods. A new neural network method ELM is selected for its better performance to establish the basic day-ahead electricity price prediction model. Using the information fusion and ensemble ideas, a multiple ELM modeling approach is proposed to establish...
Financial distress is the most synthetic form of business crisis and financial distress prediction (FDP) has been a widely and continually studied topic in the field of corporate finance. This paper attempts to put forward OR-CBR in K-nearest neighbors model, which can be the implementation of corresponding algorithm.
The textual content of company annual reports has proven to contain predictive indicators for the company future performance. This paper addresses the general research question of evaluating the effectiveness of applying machine learning and text mining techniques to building predictive models with annual reports. More specifically, we focus on these two questions: (1) can the advantages of the ranking...
This paper presents short-term forecasting model for crude oil prices based on three layer feedforward neural network. Careful attention was paid on finding the optimal network structure. Moreover, a number of features were tested as an inputs such as crude oil futures prices, dollar index, gold spot price, heating oil spot price and S&P 500 index. The results show that with adequate network design...
Nowadays, huge amounts of information from different industrial processes are stored into databases and companies can improve their production efficiency by mining some new knowledge from this information. However, when these databases becomes too large, it is not efficient to process all the available data with practical data mining applications. As a solution, different approaches for intelligent...
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