As big data becomes more of cliche with every passing day, do you feel Internet of Things is the next marketing buzzword to grapple our lives. So what exactly is Internet of Thing (IoT) and why are we going to hear more about it in the coming days. Internet of thing (IoT) today denotes advanced connectivity of devices,systems and services that goes beyond machine to machine communications and covers a wide variety of domains and applications specifically in the manufacturing and power, oil and gas utilities. An application in IoT can be an automobile that has built in sensors to alert the driver when the tyre pressure is low. Built-in sensors on equipment's present in the power plant which transmit real time data and thereby enable to better transmission planning,load balancing.
One of the topics in data science or statistics I found interesting, but having difficulty understanding is Bayesian analysis. During the course of my General Assembly's Data Science Immersive boot camp, I have had a chance to explore Bayesian statistics, but I really think I need some review and reinforcement. This is my personal endeavour to have a better understanding of Bayesian thinking, and how it can be applied to real-life cases. For this post, I am mainly inspired by a Youtube series by Rasmus Bååth, "Introduction to Bayesian data analysis". He is really good at giving you an intuitive understanding of Bayesian analysis, not by bombarding you with all the complicated formulas, but by providing you with a thought-process of Bayesian statistics. The topic I chose for this post is baseball.
Holst, Anders (Swedish Institute of Computer Science) | Bohlin, Markus (Swedish Institute of Computer Science) | Ekman, Jan (Swedish Institute of Computer Science) | Sellin, Ola (Bombardier Transportation) | Lindström, Björn (Addiva Consulting AB) | Larsen, Stefan (Addiva Eduro AB)
We have developed a method for statistical anomaly detection which has been deployed in a tool for condition monitoring of train fleets. The tool is currently used by several railway operators over the world to inspect and visualize the occurrence of event messages generated on the trains. The anomaly detection component helps the operators to quickly find significant deviations from normal behavior and to detect early indications for possible problems. The savings in maintenance costs comes mainly from avoiding costly breakdowns, and have been estimated to several million Euros per year for the tool. In the long run, it is expected that maintenance costs can be reduced with between 5 and 10 % by using the tool.
Data-driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model. A threshold is invariably needed to identify data instances with high (or low) scores as anomalies. This presents a practical limitation on the applicability of such methods, since most methods are sensitive to the choice of the threshold, and it is challenging to set optimal thresholds. We present a probabilistic framework to explicitly model the normal and anomalous behaviors and probabilistically reason about the data. An extreme value theory based formulation is proposed to model the anomalous behavior as the extremes of the normal behavior. As a specific instantiation, a joint non-parametric clustering and anomaly detection algorithm (INCAD) is proposed that models the normal behavior as a Dirichlet Process Mixture Model. A pseudo-Gibbs sampling based strategy is used for inference. Results on a variety of data sets show that the proposed method provides effective clustering and anomaly detection without requiring strong initialization and thresholding parameters.