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ردیف | عنوان | نوع |
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1 |
Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices
روش مبتنی بر یادگیری عمیق بیزی برای پیش بینی احتمالی قیمت برق روز پیش رو-2019 The availability of accurate day-ahead energy prices forecasts is crucial to achieve a successful participation to
liberalized electricity markets. Moreover, forecasting systems providing prediction intervals and densities (i.e.
probabilistic forecasting) are fundamental to enable enhanced bidding and planning strategies considering
uncertainty explicitly. Nonetheless, the vast majority of available approaches focus on point forecast. Therefore,
we propose a novel methodology for probabilistic energy price forecast based on Bayesian deep learning
techniques. A specific training method has been deployed to guarantee scalability to complex network architectures.
Moreover, we developed a model originally supporting heteroscedasticity, thus avoiding the common
homoscedastic assumption with related preprocessing effort. Experiments have been performed on two dayahead
markets characterized by different behaviors. Then, we demonstrated the capability of the proposed
method to achieve robust performances in out-of-sample conditions while providing forecast uncertainty indications.. Keywords: Electricity price forecasting | Probabilistic forecasting | Deep learning | Bayesian learning | Neural network |
مقاله انگلیسی |
2 |
A tale of two uncertainties
یک حکایتی از دو عدم قطعیت-2018 Consistent with Bayesian learning models, I find that two types of uncertainty—market uncertainty and firm-signal uncertainty—have opposite effects on investors’ learning from new information. I provide novel evidence that investor learning increases with the level of prior market uncertainty and decreases with firm-signal uncertainty (i.e., signal precision). Specifically, I find that the stock price response to earnings announcements increases with market volatility and decreases with earnings volatility. The results indicate that investor learning increases linearly with market uncertainty and decreases nonlinearly with firm-signal uncertainty. The effect of market uncertainty is stronger for large firms, firms with more market information in their returns, and firms with more institutional ownership.
keywords: Market uncertainty |Firm-signal uncertainty |Bayesian learning |Earnings announcements |Stock price responses |
مقاله انگلیسی |
3 |
Hierarchical topic modeling with automatic knowledge mining
مدل سازی موضوع سلسله مراتبی با دانش کاوی -2018 Traditional topic modeling has been widely studied and popularly employed in expert systems and in
formation systems. However, traditional topic models cannot discover structural relations among topics,
thus losing the chance to explore the data more deeply. Hierarchical topic modeling has the capability
of learning topics, as well as discovering the hierarchical topic structure from text data. But purely un
supervised models tend to generate weak topic hierarchies. To solve this problem, we propose a novel
knowledge-based hierarchical topic model (KHTM), which can incorporate prior knowledge into topic hi
erarchy building. A key novelty of this model is that it can mine prior knowledge automatically from the
topic hierarchies of multiple domains corpora. In this paper, the knowledge is represented as the word
pairs which satisfy the requirement of frequent co-occurrence, and knowledge is organized in form of hi
erarchical structure. We also propose an iterative learning algorithm. For evaluation, we crawled two new
multi-domain datasets and conducted comprehensive experiments. The experimental results show that
our algorithm and model can generate more coherent topics, and more reasonable hierarchical structure.
Keywords: Hierarchical topic modeling ، Text mining ، Knowledge mining ، Non-parametric Bayesian learning ، Gibbs sampling |
مقاله انگلیسی |
4 |
Upper bound of Bayesian generalization error in non-negative matrix factorization
مرز بالایی خطای تعمین بیزی در عامل بندی ماتریس غیرمنفی -2017 Non-negative matrix factorization (NMF) is a new knowledge discovery method that is used for text min
ing, signal processing, bioinformatics, and consumer analysis. However, its basic property as a learning
machine is not yet clarified, as it is not a regular statistical model, resulting that theoretical optimiza
tion method of NMF has not yet established. In this paper, we study the real log canonical threshold of
NMF and give an upper bound of the generalization error in Bayesian learning. The results show that
the generalization error of the matrix factorization can be made smaller than regular statistical models if
Bayesian learning is applied.
Keywords: Non-negative matrix factorization (NMF) | Real log canonical threshold (RLCT) | Bayesian learning |
مقاله انگلیسی |