Capturing Evolution Genes for Time Series Data

The modeling of time series is becoming increasingly critical in a wide variety of applications. Overall, data evolves by following different patterns, which are generally caused by different user behaviors. Given a time series, we define the evolution gene to capture the latent user behaviors and to describe how the behaviors lead to the generation of time series. In particular, we propose a uniform framework that recognizes different evolution genes of segments by learning a classifier, and adopt an adversarial generator to implement the evolution gene by estimating the segments' distribution. Experimental results based on a synthetic dataset and five real-world datasets show that our approach can not only achieve a good prediction results (e.g., averagely +10.56% in terms of F1), but is also able to provide explanations of the results.Comment: a preprint version. arXiv admin note: text overlap with arXiv:1703.10155 by other author

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the pattern by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game makes a decision among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information

Diffusion of Lexical Change in Social Media

Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity -- especially with regard to race -- plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified "netspeak" dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English.Comment: preprint of PLOS-ONE paper from November 2014; PLoS ONE 9(11) e11311

Dynamic Economic Relationships Among U.S. Soy Product Markets: Using a Cointegrated Vector Autoregression Approach with Directed Acyclic Graphs

This paper applies a combined methodology of a recently developed directed acyclic graph (DAG) analysis with Johansen and Juselius' methods of the cointegrated vector autoregression (VAR) model to a monthly U.S. system of markets for soybeans, soy meal, and soy oil. Primarily a methods paper, Johansen and Juselius' procedures are applied, with a special focus on statistically addressing information inherent in well-known sources of non-normal data behavior to illustrate the effectiveness of modeling the system as a cointegrated multi-market system. Perhaps for the first time, methods of the cointegrated VAR model are combined with DAG analysis to account for contemporaneously correlated residuals, and are applied to this U.S. soy-based system. Analysis of the error correction or cointegration space illuminates the empirical nature of policy-relevant market elasticities, price transmission parameters, and effects of important policy and institutional changes/events on U.S. soy-related markets at long-run horizons beyond a single crop cycle. A statistically strong U.S. demand for soybeans emerged as the primary cointegrating relation in the error-correction space. Analysis of the DAG-adjusted cointegrated VAR model's forecast error variance decomposition illuminates how the soy-related variables and the three U.S. soy product markets dynamically interact at alternative time horizons extending up to two-years.directed acyclic graphs, cointegration, vector error correction and vector autoregression models, monthly U.S. soy-based markets., Industrial Organization, Research Methods/ Statistical Methods,