PPT-Analysis of Causal Topics in Text Data and Time Series with Applications to Presidential

Hyun Duk Kim ChengXiang Cheng Zhai UIUC Thomas A Rietz Univ of Iowa Daniel Diermeier Northwestern Univ Meichun Hsu Malu Castellanos and Carlos . Basic time series. Data on the outcome of a variable or variables in different time periods are known as time-series data.. Time-series data are prevalent in finance and can be particularly challenging because. By Zhangzhou. Introduction&Background. Time-Series Data. Conception & Examples & Features. Time-Series Model. Static model. Y. t. = β. 0. + β. z. t. + . μ. t. Finite Distributed Lag . Graphcial. Causal Models. Richard . Scheines. Joe Ramsey. Carnegie Mellon University. Peter Spirtes, Clark Glymour. Goals. Convey rudiments of graphical causal models. Basic working knowledge of Tetrad IV. Abstract. In many real-world applications, it is important to mine causal relationships where an event or event pattern causes certain outcomes with low probability. Discovering this kind of causal relationships can help us prevent or correct negative outcomes caused by their antecedents. In this paper, we propose an innovative data mining framework and apply it to mine potential causal associations in electronic patient data sets where the drug-related events of interest occur infrequently. Specifically, we created a novel interestingness measure, exclusive causal-leverage, based on a computational, fuzzy recognition-primed decision (RPD) model that we previously developed. On the basis of this new measure, a data mining algorithm was developed to mine the causal relationship between drugs and their associated adverse drug reactions (ADRs). . -- An Introduction --. 1. AMS . 586. Objectives of time series analysis. Data description. Data interpretation. Modeling. Control. Prediction & Forecasting. 2. Time-Series Data. Numerical data obtained at regular time intervals. Introduction to Time Series Analysis. A . time-series. is a set of observations on a quantitative variable collected over time.. Examples. Dow Jones Industrial Averages. Historical data on sales, inventory, customer counts, interest rates, costs, etc. Graphcial. Causal Models. Richard . Scheines. Joe Ramsey. Carnegie Mellon University. Peter Spirtes, Clark Glymour. Goals. Convey rudiments of graphical causal models. Basic working knowledge of Tetrad IV. Data . Mining Algorithm. Peter Myers. Bitwise Solutions Pty Ltd. DBI-B326. Presenter Introduction. Peter Myers. BI Expert, Bitwise Solutions Pty Ltd. BBus. , SQL Server MCSE, MCT, SQL Server MVP (since 2007). PRESENTED BY. Malit. . Keldine. . Owande. ……………..………………………I07/2930/2009. June Agnes . Njeri. Mwangi…………………………………I07/28782/2009. Omondi Joseph . with recurrent neural networks. Aymen. . Cherif. . , Hubert . Cardot. , . Romuald. Bone. 2011, . Necurocomputing. Presented by . Chien-Hao. Kung. 2011/11/3. 2. Outlines. Motivation. Objectives. Methodology. COS 418: Distributed Systems. Lecture . 14. Wyatt Lloyd. Consistency Hierarchy. Linearizability. Sequential Consistency. Causal+ Consistency. Eventual Consistency. e.g., RAFT. e.g., Bayou. e.g., Dynamo. STAT 689. forecasting. Forecasting is the process of making predictions of the future based on past and present data!. forecasting. Coming up with predictions is important.. It is also very hard since none has the correct model of the world.. Dr . Milena . Čukić. Dpt. General Physiology with Biophysics. University of Belgrade, Serbia. Complex dynamics of living systems. Living organisms are complex both in their structures and functions. Parameters of human physiological functions such as arterial blood pressure (. Authors: Aditya Stanam. 2* . & Shrikant Pawar. 3* . Addresses: . 2. Department. of Toxicology, University of Iowa. , Iowa City, Iowa 52242-5000 . 3. School of Medicine, Yale University, New Haven, Connecticut, 30303, USA.