Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

Low-cost renewable electricity as the key driver of the global energy transition towards sustainability

Thank you very much for reading technical analysis from a to z. Maybe you have knowledge that, people have search numerous times for their favorite books like this technical analysis from a to z, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some infectious bugs inside their computer. technical analysis from a to z is available in our digital library an online access to it is set as public so you can get it instantly. Our books collection hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the technical analysis from a to z is universally compatible with any devices to read.

Technical Analysis From A To Z

The paper focuses on the analysis of the cryptocurrency open innovation market to predict sustainable growth in the future. The nature of cryptocurrencies ‘development leads to the rapid increase in their popularity and spread of trading at this new market. The high volatility of these assets is encouraging to understand and predict their price in ever changing market environment. The paper proposed the pool complexity approach to choose optimal technology using social activity in the internet, trading parameters, technical indicators and other cryptocurrency data. According to the results of the analysis, the most effective and promising cryptocurrency is EOS cryptocurrency, which has the lowest complexity and commission level among the analyzed digital currencies and allows you to implement third-party applications in the system.

Cryptocurrency Market Analysis from the Open Innovation Perspective

In this literature review, we investigate machine learning techniques that are applied for stock market prediction. A focus area in this literature review is the stock markets investigated in the literature as well as the types of variables used as input in the machine learning techniques used for predicting these markets. We examined 138 journal articles published between 2000 and 2019. The main contributions of this review are: (1) an extensive examination of the data, in particular, the markets and stock indices covered in the predictions, as well as the 2173 unique variables used for stock market predictions, including technical indicators, macro-economic variables, and fundamental indicators, and (2) an in-depth review of the machine learning techniques and their variants deployed for the predictions. In addition, we provide a bibliometric analysis of these journal articles, highlighting the most influential works and articles. 

Machine learning techniques and data for stock market forecasting: A literature review

Connecting research on the water demand of power plants with mitigation strategies for energy-based water use is an important step to ensure global water and energy security, and thus provide more sustainable use of both. Here, we assess the water footprint of 13,863 thermal power plants units with a total active capacity of 4,182 GW worldwide and give an estimate of the current water demand for power production at four different levels—global, regional, country and river. Furthermore, we provide a projection for the energy transition period towards a net zero greenhouse gas emissions economy by 2050. In particular, we show that by following a ‘Best Policies Scenario’ the water consumption of global power plants can be decreased by about 98%, and water withdrawal by 95% by 2050. Therefore, the suggested pathway provides one potential solution to the problem of water depletion that results from the water-energy nexus. Cooling in thermal power plants demands significant quantities of freshwater globally. Using a database of cooling technologies for 13,863 thermal power plants worldwide identified from satellite imagery, Lohrmann et al. show significant potential for reducing water use in power production as a consequence of transition towards renewables.

/pdf/global-scenarios-for-significant-water-use-reduction-in-4gn8wpegqq.pdf

Global scenarios for significant water use reduction in thermal power plants based on cooling water demand estimation using satellite imagery

Classification of intraday S&P500 returns with a Random Forest

Large amounts of information and various features are in many machine learning applications available, or easily obtainable. However, their quality is potentially low and greater volumes of information are not always beneficial for machine learning, for instance, when not all available features in a data set are relevant for the classification task and for understanding the studied phenomenon. Feature selection aims at determining a subset of features that represents the data well, gives accurate classification results and reduces the impact of noise on the classification performance. In this paper, we propose a filter feature ranking method for feature selection based on fuzzy similarity and entropy measures (FSAE), which is an adaptation of the idea used for the wrapper function by Luukka (2011) and has an additional scaling factor. The scaling factor to the feature and class-specific entropy values that is implemented, accounts for the distance between the ideal vectors for each class. Moreover, a wrapper version of the FSAE with a similarity classifier is presented as well. The feature selection method is tested on five medical data sets: dermatology, chronic kidney disease, breast cancer, diabetic retinopathy and horse colic. The wrapper version of FSAE is compared to the wrapper introduced by Luukka (2011) and shows at least as accurate results with often considerably fewer features. In the comparison with ReliefF, Laplacian score, Fisher score and the filter version of Luukka (2011), the FSAE filter in general achieves competitive mean accuracies and results for one medical data set, the breast cancer Wisconsin data set, together with the Laplacian score in the best results over all possible feature removals.

A combination of fuzzy similarity measures and fuzzy entropy measures for supervised feature selection

In the literature, researchers and practitioners can find a manifold of algorithms to perform a classification task. The similarity classifier is one of the more recently suggested classification algorithms. In this paper, we suggest a novel similarity classifier with multiple ideal vectors per class that are generated with k-means clustering in combination with the jump method. Two approaches for pre-processing, via simple standardization and via principal component analysis in combination with the MAP test and Parallel Analysis, are presented. On the artificial data sets, the novel classifier with standardization and with transformation power Y = 1 for the jump method results in significantly higher mean classification accuracies than the standard classifier. The results of the artificial data sets demonstrate that in contrast to the standard similarity classifier, the novel approach has the ability to cope with more complex data structures. For the real-world credit data sets, the novel similarity classifier with standardization and Y = 1 achieves competitive results or even outperforms the k-nearest neighbour classifier, the Naive Bayes algorithm, decision trees, random forests and the standard similarity classifier.

/pdf/a-novel-similarity-classifier-with-multiple-ideal-vectors-4mh1xhtaz6.pdf

Christoph Lohrmann

Papers

Machine learning techniques and data for stock market forecasting: A literature review

Global scenarios for significant water use reduction in thermal power plants based on cooling water demand estimation using satellite imagery

Classification of intraday S&P500 returns with a Random Forest

A combination of fuzzy similarity measures and fuzzy entropy measures for supervised feature selection

A novel similarity classifier with multiple ideal vectors based on k-means clustering