Time Series Data Mining: A Retail Application Using SAS Enterprise Miner

Modern technologies have allowed for the amassment of data at a rate never encountered before. Organizations are now able to routinely collect and process massive volumes of data. A plethora of regularly collected information can be ordered using an appropriate time interval. The data would thus be developed into a time series. With such data, analytical techniques can be employed to collect information pertaining to historical trends and seasonality. Time series data mining methodology allows users to identify commonalities between sets of time-ordered data. This technique is supported by a variety of algorithms, notably dynamic time warping (DTW). This mathematical technique supports the identification of similarities between numerous time series. The following research aims to provide a practical application of this methodology using SAS Enterprise Miner, an industry-leading software platform for business analytics. Due to the prevalence of time series data in retail settings, a realistic product sales transaction data set was analyzed. This information was provided by dunnhumbyUSA. Interpretations were drawn from output that was generated using “TS nodes” in SAS Enterprise Miner

Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics

Cryptocurrencies have recently experienced a new wave of price volatility and interest; activity within social media communities relating to cryptocurrencies has increased significantly. There is currently limited documented knowledge of factors which could indicate future price movements. This paper aims to decipher relationships between cryptocurrency price changes and topic discussion on social media to provide, among other things, an understanding of which topics are indicative of future price movements. To achieve this a well-known dynamic topic modelling approach is applied to social media communication to retrieve information about the temporal occurrence of various topics. A Hawkes model is then applied to find interactions between topics and cryptocurrency prices. The results show particular topics tend to precede certain types of price movements, for example the discussion of 'risk and investment vs trading' being indicative of price falls, the discussion of 'substantial price movements' being indicative of volatility, and the discussion of 'fundamental cryptocurrency value' by technical communities being indicative of price rises. The knowledge of topic relationships gained here could be built into a real-time system, providing trading or alerting signals.Comment: 3rd International Conference on Knowledge Engineering and Applications (ICKEA 2018) - Moscow, Russia (June 25-27 2018

Location Prediction: Communities Speak Louder than Friends

Humans are social animals, they interact with different communities of friends to conduct different activities. The literature shows that human mobility is constrained by their social relations. In this paper, we investigate the social impact of a person's communities on his mobility, instead of all friends from his online social networks. This study can be particularly useful, as certain social behaviors are influenced by specific communities but not all friends. To achieve our goal, we first develop a measure to characterize a person's social diversity, which we term `community entropy'. Through analysis of two real-life datasets, we demonstrate that a person's mobility is influenced only by a small fraction of his communities and the influence depends on the social contexts of the communities. We then exploit machine learning techniques to predict users' future movement based on their communities' information. Extensive experiments demonstrate the prediction's effectiveness.Comment: ACM Conference on Online Social Networks 2015, COSN 201

DYNAMIC RELATIONS AND SHARIA STOCK MARKET INTEGRATION WITH OIL PRICES (Studies: Indonesia, Malaysia, USA, UK, Japan 2012-2016)

The purpose of this research is to analyze the relationship of dynamic and integration between world sharia stock market with world crude oil price. This research can find out the integration relationship between world sharia stock market with world crude oil price. The object of this research is sharia stock market in Indonesia, Malaysia, United States, UK, Japan during period 2012-2016. The research method is Dynamic Coditional Correlation Multivariate-GARCH method is used to test the hypothesis in order to know the relationship of sharia stock market integration in world with world oil price. In this case to test the conditional correlation multivariate-GARCH method, reasearcher have taken any steps is descriptive statistical testing, heteroskedasticity testing, stationary test, and GARCH univariate testing. The result of the research shows that there is a significant dynamic correlation in world sharia stock price (Indonesia, Malaysia, United States, United Kingdom, Japan) and significant dynamic relationship between world sharia stock market with world crude oil price. It can be explained indirectly proves the existence of integration relationship between world sharia stock market with world crude oil price. Keywords: sharia stocks integration, sharia stock price, world crude oil price, Dynamic Conditional Correlation Multivariate-GARCH (DCC-MGARCH)