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While attention is a predictor for digital asset prices, and jumps in Bitcoin prices are well-known, we know little about its alternatives. Studying high frequency crypto data gives us the unique possibility to confirm that cross market digital asset returns are driven by high frequency jumps clustered around black swan events, resembling volatility and trading volume seasonalities. Regressions show that intra-day jumps significantly influence end of day returns in size and direction. This provides fundamental research for crypto option pricing models. However, we need better econometric methods for capturing the specific market microstructure of cryptos. All calculations are reproducible via the quantlet.com technology.

Strategic planning in a corporate environment is often based on experience and intuition, although internal data is usually available and can be a valuable source of information. Predicting merger & acquisition (M&A) events is at the heart of strategic management, yet not sufficiently motivated by data analytics driven controlling. One of the main obstacles in using e.g. count data time series for M&A seems to be the fact that the intensity of M&A is time varying at least in certain business sectors, e.g. communications. We propose a new automatic procedure to bridge this obstacle using novel statistical methods. The proposed approach allows for a selection of adaptive windows in count data sets by detecting significant changes in the intensity of events. We test the efficacy of the proposed method on a simulated count data set and put it into action on various M&A data sets. It is robust to aberrant behaviour and generates accurate forecasts for the evaluated business sectors. It also provides guidance for an a-priori selection of fixed windows for forecasting. Furthermore, it can be generalized to other business lines, e.g. for managing supply chains, sales forecasts, or call center arrivals, thus giving managers new ways for incorporating statistical modeling in strategic planning decisions.

In recent years, research unveiled more and more evidence for the so-called Bayesian Brain Paradigm, i.e. the human brain is interpreted as a probabilistic inference machine and Bayesian modelling approaches are hence used successfully. One of the many theories is that of Probabilistic Population Codes (PPC). Although this model has so far only been considered as meaningful and useful for sensory perception as well as motor control, it has always been suggested that this mechanism also underlies higher cognition and decision-making. However, the adequacy of PPC for this regard cannot be confirmed by means of neurological standard measurement procedures. In this article we combine the parallel research branches of recommender systems and predictive data mining with theoretical neuroscience. The nexus of both fields is given by behavioural variability and resulting internal distributions. We adopt latest experimental settings and measurement approaches from predictive data mining to obtain these internal distributions, to inform the theoretical PPC approach and to deduce medical correlates which can indeed be measured in vivo. This is a strong hint for the applicability of the PPC approach and the Bayesian Brain Paradigm for higher cognition and human decision-making., Comment: arXiv admin note: text overlap with arXiv:1804.10861

Recent research revealed a considerable lack of reliability for user feedback when interacting with adaptive systems, often denoted as user noise or human uncertainty. Moreover, this lack of reliability holds striking impacts for the assessment of adaptive systems and personalisation approaches. Whenever research on this topic is done, there is a very strong system-centric view in which user variation is something undesirable and should be modelled with the eye to eliminate. However, the possibilities of extracting additional information were only insufficiently considered so far. In this contribution we consider the neuroscientific theory of the Bayesian brain in order to develop novel user models with the power of turning the variability of user behaviour into additional information for improving recommendation and personalisation. To this end, we first introduce an adaptive model in which populations of neurons provide an estimation for a feedback to be submitted. Subsequently, we present various decoder functions with which neuronal activity can be translated into quantitative decisions. The interplay of cognition model and decoder functions lead to different model-based properties of decision-making. This will help to associate users to different clusters on the basis of their individual neural characteristics and thinking patterns. By means of user experiments and simulations, we show that this information can be used to improve the standard collaborative filtering., Comment: User Noise, Human Uncertainty, Collaborative Filtering, User Models, Bayesian Brain, Probabilistic Population Codes, Cognitive Agency, Neural Coding

In this paper we consider the neuroscientific theory of the Bayesian brain in the light of adaptive web systems and content personalisation. In particular, we elaborate on neural mechanisms of human decision-making and the origin of lacking reliability of user feedback, often denoted as noise or human uncertainty. To this end, we first introduce an adaptive model of cognitive agency in which populations of neurons provide an estimation for states of the world. Subsequently, we present various so-called decoder functions with which neuronal activity can be translated into quantitative decisions. The interplay of the underlying cognition model and the chosen decoder function leads to different model-based properties of decision processes. The goal of this paper is to promote novel user models and exploit them to naturally associate users to different clusters on the basis of their individual neural characteristics and thinking patterns. These user models might be able to turn the variability of user behaviour into additional information for improving web personalisation and its experience., Comment: Bayesian Brain, Neural Coding, Human Uncertainty, Noise, User Models

Latest research revealed a considerable lack of reliability within user feedback and discussed striking impacts for the assessment of adaptive web systems and content personalisation approaches, e.g. ranking errors, systematic biases to accuracy metrics as well as its natural offset (the magic barrier). In order to perform holistic assessments and to improve web systems, a variety of strategies have been proposed to deal with this so-called human uncertainty. In this contribution we discuss the most relevant strategies to handle uncertain feedback and demonstrate that these approaches are more or less ineffective to fulfil their objectives. In doing so, we consider human uncertainty within a purely probabilistic framework and utilise hypothesis testing as well as a generalisation of the magic barrier to compare the effects of recently proposed algorithms. On this basis we recommend a novel strategy of acceptance which turns away from mere filtering and discuss potential benefits for the community of the WWW., Comment: Human Uncertainty, Noise, Magic Barrier, Ranking Error, User Feedback

One of the most crucial issues in data mining is to model human behaviour in order to provide personalisation, adaptation and recommendation. This usually involves implicit or explicit knowledge, either by observing user interactions, or by asking users directly. But these sources of information are always subject to the volatility of human decisions, making utilised data uncertain to a particular extent. In this contribution, we elaborate on the impact of this human uncertainty when it comes to comparative assessments of different data mining approaches. In particular, we reveal two problems: (1) biasing effects on various metrics of model-based prediction and (2) the propagation of uncertainty and its thus induced error probabilities for algorithm rankings. For this purpose, we introduce a probabilistic view and prove the existence of those problems mathematically, as well as provide possible solution strategies. We exemplify our theory mainly in the context of recommender systems along with the metric RMSE as a prominent example of precision quality measures.

In this paper, we examine the statistical soundness of comparative assessments within the field of recommender systems in terms of reliability and human uncertainty. From a controlled experiment, we get the insight that users provide different ratings on same items when repeatedly asked. This volatility of user ratings justifies the assumption of using probability densities instead of single rating scores. As a consequence, the well-known accuracy metrics (e.g. MAE, MSE, RMSE) yield a density themselves that emerges from convolution of all rating densities. When two different systems produce different RMSE distributions with significant intersection, then there exists a probability of error for each possible ranking. As an application, we examine possible ranking errors of the Netflix Prize. We are able to show that all top rankings are more or less subject to high probabilities of error and that some rankings may be deemed to be caused by mere chance rather than system quality.

In this paper we consider the modern theory of the Bayesian brain from cognitive neurosciences in the light of recommender systems and expose potentials for our community. In particular, we elaborate on noisy user feedback and the thus resulting multicomponent user models, which have indeed a biological origin. In real user experiments we observe the impact of both factors directly in a repeated rating task along with recommendation. As a consequence, this contribution supports the plausibility of contemporary theories of mind in the context of recommender systems and can be understood as a solicitation to integrate ideas of cognitive neurosciences into our systems in order to further improve the prediction of human behaviour.

Recommender systems nowadays have many applications and are of great economic benefit. Hence, it is imperative for success-oriented companies to compare different of such systems and select the better one for their purposes. To this end, various metrics of predictive accuracy are commonly used, such as the Root Mean Square Error (RMSE), or precision and recall. All these metrics more or less measure how well a recommender system can predict human behaviour. Unfortunately, human behaviour is always associated with some degree of uncertainty, making the evaluation difficult, since it is not clear whether a deviation is system-induced or just originates from the natural variability of human decision making. At this point, some authors speculated that we may be reaching some Magic Barrier where this variability prevents us from getting much more accurate. In this article, we will extend the existing theory of the Magic Barrier into a new probabilistic but a yet pragmatic model. In particular, we will use methods from metrology and physics to develop easy-to-handle quantities for computation to describe the Magic Barrier for different accuracy metrics and provide suggestions for common application. This discussion is substantiated by comprehensive experiments with real users and large-scale simulations on a high-performance cluster.

In many areas of data mining, data is collected from humans beings. In this contribution, we ask the question of how people actually respond to ordinal scales. The main problem observed is that users tend to be volatile in their choices, i.e. complex cognitions do not always lead to the same decisions, but to distributions of possible decision outputs. This human uncertainty may sometimes have quite an impact on common data mining approaches and thus, the question of effective modelling this so called human uncertainty emerges naturally. Our contribution introduces two different approaches for modelling the human uncertainty of user responses. In doing so, we develop techniques in order to measure this uncertainty at the level of user inputs as well as the level of user cognition. With support of comprehensive user experiments and large-scale simulations, we systematically compare both methodologies along with their implications for personalisation approaches. Our findings demonstrate that significant amounts of users do submit something completely different (action) than they really have in mind (cognition). Moreover, we demonstrate that statistically sound evidence with respect to algorithm assessment becomes quite hard to realise, especially when explicit rankings shall be built.

Many data mining approaches aim at modelling and predicting human behaviour. An important quantity of interest is the quality of model-based predictions, e.g. for finding a competition winner with best prediction performance. In real life, human beings meet their decisions with considerable uncertainty. Its assessment and resulting implications for statistically evident evaluation of predictive models are in the main focus of this contribution. We identify relevant sources of uncertainty as well as the limited ability of its accurate measurement, propose an uncertainty-aware methodology for more evident evaluations of data mining approaches, and discuss its implications for existing quality assessment strategies. Specifically, our approach switches from common point-paradigm to more appropriate distribution-paradigm. This is exemplified in the context of recommender systems and their established metrics of prediction quality. The discussion is substantiated by comprehensive experiments with real users, large-scale simulations, and discussion of prior evaluation campaigns (i.a. Netflix Prize) in the light of human uncertainty aspects.

The prediction of graph evolution is an important and challenging problem in the analysis of networks and of the Web in particular. But while the appearance of new links is part of virtually every model of Web growth, the disappearance of links has received much less attention in the literature. To fill this gap, our approach DecLiNe (an acronym for DECay of LInks in NEtworks) aims to predict link decay in networks, based on structural analysis of corresponding graph models. In analogy to the link prediction problem, we show that analysis of graph structures can help to identify indicators for superfluous links under consideration of common network models. In doing so, we introduce novel metrics that denote the likelihood of certain links in social graphs to remain in the network, and combine them with state-of-the-art machine learning methods for predicting link decay. Our methods are independent of the underlying network type, and can be applied to such diverse networks as the Web, social networks and any other structure representable as a network, and can be easily combined with case-specific content analysis and adopted for a variety of social network mining, filtering and recommendation applications. In systematic evaluations with large-scale datasets of Wikipedia we show the practical feasibility of the proposed structure-based link decay prediction algorithms.