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Machine Learning the Cryptocurrency Market
TLDR
We show that non-trivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market. Expand
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Anticipating Cryptocurrency Prices Using Machine Learning
TLDR
We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Expand
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Evidence for a conserved quantity in human mobility
TLDR
Analysing high-resolution mobility traces from almost 40,000 individuals reveals that people typically revisit a set of 25 familiar locations day-to-day, but that this set evolves over time and is proportional to their social sphere. Expand
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Random walks on activity-driven networks with attractiveness.
Virtually all real-world networks are dynamical entities. In social networks, the propensity of nodes to engage in social interactions (activity) and their chances to be selected by active nodesExpand
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Gender-specific behavior change following terror attacks
TLDR
We study the behavioral response of citizens of cities affected by $7$ different terror attacks. Expand
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Evolutionary dynamics of the cryptocurrency market
The cryptocurrency market surpassed the barrier of $100 billion market capitalization in June 2017, after months of steady growth. Despite its increasing relevance in the financial world, aExpand
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Multi-scale spatio-temporal analysis of human mobility
TLDR
We show that the distributions of distances and waiting times between consecutive locations are best described by log-normal and gamma distributions, respectively, and that natural time-scales emerge from the regularity of human mobility. Expand
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Evolutionary Dynamics of the Cryptocurrency Market
The cryptocurrency market surpassed the barrier of $100 billion market capitalization in June 2017, after months of steady growth. Despite its increasing relevance in the financial world, however, aExpand
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User-based representation of time-resolved multimodal public transportation networks
TLDR
We provide a novel user-based representation of public transportation systems, which combines representations, accounting for the presence of multiple lines and reducing the effect of spatial embeddedness, while considering the total travel time, its variability across the schedule, and the number of transfers necessary. Expand
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Understanding the interplay between social and spatial behaviour
TLDR
We identify a connection between the way in which individuals explore new resources and exploit known assets in the social and spatial spheres. Expand
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