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Anomaly Detection: Algorithms, Explanations, Applications
 
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Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 8842 Microsoft Research
Machine Learning for Real-Time Anomaly Detection in Network Time-Series Data - Jaeseong Jeong
 
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Real-time anomaly detection plays a key role in ensuring that the network operation is under control, by taking actions on detected anomalies. In this talk, we discuss a problem of the real-time anomaly detection on a non-stationary (i.e., seasonal) time-series data of several network KPIs. We present two anomaly detection algorithms leveraging machine learning techniques, both of which are able to adaptively learn the underlying seasonal patterns in the data. Jaeseong Jeong is a researcher at Ericsson Research, Machine Learning team. His research interests include large-scale machine learning, telecom data analytics, human behavior predictions, and algorithms for mobile networks. He received the B.S., M.S., and Ph.D. degrees from Korea Advanced Institute of Science and Technology (KAIST) in 2008, 2010, and 2014, respectively.
Views: 12766 RISE SICS
Machine Learning for Time Series Data in Python | SciPy 2016 | Brett Naul
 
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The analysis of time series data is a fundamental part of many scientific disciplines, but there are few resources meant to help domain scientists to easily explore time course datasets: traditional statistical models of time series are often too rigid to explain complex time domain behavior, while popular machine learning packages deal almost exclusively with 'fixed-width' datasets containing a uniform number of features. Cesium is a time series analysis framework, consisting of a Python library as well as a web front-end interface, that allows researchers to apply modern machine learning techniques to time series data in a way that is simple, easily reproducible, and extensible.
Views: 38555 Enthought
"Real-Time Anomaly Detection on Time-Series IoT Sensor Data Using Deep Learning", Romeo Kienzler
 
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"Real-Time Anomaly Detection on Time-Series IoT Sensor Data Using Deep Learning", Romeo Kienzler, Chief Data Scientist at IBM Watson IoT Screen Recording can be found here: http://bit.ly/2fRjN4D Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo Visit the conference website to learn more: www.datanatives.io Follow Data Natives: https://www.facebook.com/DataNatives https://twitter.com/DataNativesConf Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2016: http://bit.ly/1WMJAqS About the Author: Romeo Kienzler works as Chief Data Scientist in the IBM Cloud Transformation Lab Zurich, he holds a M. Sc. Degree from the Swiss Federal Institute of Technology in Information Systems, Bioinformatics and Applied Statistics. He is a big fan of Open Source and the Apache Software Foundation.
Views: 7042 Data Natives
Approaches for Sequence Classification on Financial Time Series Data
 
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Sequence classification tasks can be solved in a number of ways, including both traditional ML and deep learning methods. Catch Lauren Tran’s talk at the Women in Machine Learning and Data Science meetup as she discusses the general LSTM, CNN, and SVM algorithms, how they work, and how they are applied in sequence labeling tasks with time series data. She'll walk through a practical application of applying these algorithms and techniques to financial transaction data to detect signs of financial distress and predict insolvency.
Views: 2244 Microsoft Developer
Time Series data Mining Using the Matrix Profile part 1
 
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Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins Part 1 Authors: Abdullah Al Mueen, Department of Computer Science, University of New Mexico Eamonn Keogh, Department of Computer Science and Engineering, University of California, Riverside Abstract: The Matrix Profile (and the algorithms to compute it: STAMP, STAMPI, STOMP, SCRIMP and GPU-STOMP), has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability. In particular it has implications for time series motif discovery, time series joins, shapelet discovery (classification), density estimation, semantic segmentation, visualization, clustering etc. Link to tutorial: http://www.cs.ucr.edu/~eamonn/MatrixProfile.html More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 1837 KDD2017 video
027 Anomaly detection in R
 
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Data Science Foundations: Data Mining http://bc.vc/jSMxfA3
Views: 3604 Tukang Leding
Time Series Classification Using Wavelet Scattering Transform
 
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This is a ~3-minute video highlight produced by undergraduate students Charlie Tian and Christina Coley regarding their research topic during the 2017 AMALTHEA REU Program at Florida Institute of Technology in Melbourne, FL. They were mentored by doctoral student Kaylen Bryan and professor Dr. Adrian Peter (Engineering Systems Department). More details about their project can be found at http://www.amalthea-reu.org.
What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning
 
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What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning - ANOMALY DETECTION definition - ANOMALY DETECTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In data mining, anomaly detection (also outlier detection) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset.[1] Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.[2] In particular in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular unsupervised methods) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro clusters formed by these patterns.[3] Three broad categories of anomaly detection techniques exist.[1] Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then testing the likelihood of a test instance to be generated by the learnt model.
Views: 4902 The Audiopedia
Generic and Scalable Framework for Automated Time-series Anomaly Detection
 
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Authors: Nikolay Laptev, Saeed Amizadeh, Ian Flint Abstract: This paper introduces a generic and scalable framework for automated anomaly detection on large scale time-series data. Early detection of anomalies plays a key role in maintaining consistency of person's data and protects corporations against malicious attackers. Current state of the art anomaly detection approaches suffer from scalability, use-case restrictions, difficulty of use and a large number of false positives. Our system at Yahoo, EGADS, uses a collection of anomaly detection and forecasting models with an anomaly filtering layer for accurate and scalable anomaly detection on time-series. We compare our approach against other anomaly detection systems on real and synthetic data with varying time-series characteristics. We found that our framework allows for 50-60% improvement in precision and recall for a variety of use-cases. Both the data and the framework are being open-sourced. The open-sourcing of the data, in particular, represents the first of its kind effort to establish the standard benchmark for anomaly detection. ACM DL: http://dl.acm.org/citation.cfm?id=2788611 DOI: http://dx.doi.org/10.1145/2783258.2788611
Detecting outliers and anomalies in realtime at Datadog - Homin Lee (OSCON Austin 2016)
 
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Monitoring even a modestly sized systems infrastructure quickly becomes untenable without automated alerting. For many metrics, it is nontrivial to define ahead of time what constitutes “normal” versus “abnormal” values. This is especially true for metrics whose baseline value fluctuates over time. To make this problem more tractable, Datadog provides outlier detection functionality to automatically identify any host (or group of hosts) that is behaving abnormally compared to its peers and anomaly detection to alert when any single metric is behaving differently than its past history would suggest. Homin Lee discusses the algorithms and open source tools Datadog uses for outlier and anomaly detection and lessons learned from using these alerts on its own systems, along with some real-life examples on how to avoid false positives and negatives.
Views: 11060 Datadog
Anomaly Detection - Nick Radcliffe
 
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PyData London 2018 Stochastic Solutions is producing a course on Anomaly Detection in Python for DataCamp. This workshop will give a preview of part of that course. Topics covered will include characterizing normality and abnormality, spotting anomalies by eye, building automated anomaly detectors over various kinds of data streams and types, and considerations for monitoring (false positives vs. false negatives). Slides: https://github.com/tdda/pydatalondon2018ad/blob/master/AnomalyDetectionPyDataLondon.pdf --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 2420 PyData
10.1: Time Series Data Encoding for Deep Learning, TensorFlow and Keras (Module 10, Part 1)
 
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How to represent data for time series neural networks. This includes recurrent neural network (RNN) types of LSTM and GRU. This video is part of a course that is taught in a hybrid format at Washington University in St. Louis; however, all the information is online and you can easily follow along. T81-558: Application of Deep Learning, at Washington University in St. Louis Please subscribe and comment! Follow me: YouTube: https://www.youtube.com/user/HeatonResearch Twitter: https://twitter.com/jeffheaton GitHub: https://github.com/jeffheaton More links: Complete course: https://sites.wustl.edu/jeffheaton/t81-558/ Complete playlist: https://www.youtube.com/playlist?list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN
Views: 4721 Jeff Heaton
Time Series data Mining Using the Matrix Profile part 2
 
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Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins Part 2 Authors: Abdullah Al Mueen, Department of Computer Science, University of New Mexico Eamonn Keogh, Department of Computer Science and Engineering, University of California, Riverside Abstract: The Matrix Profile (and the algorithms to compute it: STAMP, STAMPI, STOMP, SCRIMP and GPU-STOMP), has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability. In particular it has implications for time series motif discovery, time series joins, shapelet discovery (classification), density estimation, semantic segmentation, visualization, clustering etc. Link to tutorial: http://www.cs.ucr.edu/~eamonn/MatrixProfile.html More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 712 KDD2017 video
Time series anomaly detection in real time.
 
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This shows an example of real-time time series anomaly discovery with rule density curve built using sliding window-based SAX discretization and grammatical inference with Sequitur. Our paper describing the approach: http://csdl.ics.hawaii.edu/techreports/2014/14-05/14-05.pdf (SAX parameters used: window 400, PAA size 8, Alphabet size 6)
Views: 4344 seninp
PyCon.DE 2017 Nils Braun - Time series feature extraction with tsfresh - “get rich or die..
 
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Time series feature extraction with tsfresh - “get rich or die overfitting” Nils Braun (@_nilsbraun) Currently I am doing my PhD in Particle Physics - which mainly involves development of software in a large collaboration. I love working with Python and C++ to process large amounts of data. Of course it needs to be processed as quickly as possible. I am working on the core reconstruction algorithms for our experiment, which are steered and controlled using Python. Apart from that, I was working as a Data Science Engineer for Blue Yonder, a leading machine learning company, where the idea for tsfresh was born. I am still heavily involved in the project. When I am not writing code, I am updating myself on the newest technical geek stuff (mostly cloud computing and deep learning) or play the guitar. Abstract Tags: pydata time series data-science machine learning python ai Have you ever thought about developing a time series model to predict stock prices? Or do you consider log time series from the operation of cloud resources as being more compelling? In this case you really should consider using the time series feature extraction package tsfresh for your project. Description Trends such as the Internet of Things (IoT), Industry 4.0, and precision medicine are driven by the availability of cheap sensors and advancing connectivity, which among others increases the availability of temporally annotated data. The resulting time series are the basis for manifold machine learning applications. Examples are the classification of hard drives into risk classes concerning specific defect, the log analysis of server farms for detecting intruders, or regression tasks like the prediction of the remaining lifespan of machinery. Tsfresh also allows to easily setup a machine learning pipeline that predicts stock prices, which we will demonstrate live during the presentation ;). The problem of extracting and selecting relevant features for classification or regression is these domains is especially hard to solve, if each label or regression target is associated with several time series and meta-information simultaneously – which is a common pattern in industrial applications. This talk introduces a distributed and parallel feature extraction and selection algorithm – the recently published Python library tsfresh. The fully automated extraction and importance selection does not only allow to reach better machine learning classification scores, but in combination with the speed of the package, also allows to incorporate tsfresh into automated AI-pipelines. Recorded at PyCon.DE 2017 Karlsruhe: pycon.de Video editing: Sebastian Neubauer & Andrei Dan Tools: Blender, Avidemux & Sonic Pi
Views: 2478 PyConDE
Bugra Akyildiz: Trend Estimation in Time Series Signals
 
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PyData Seattle 2015 Trend estimation is a family of methods to be able to detect and predict tendencies and regularities in time series signals without knowing any information a priori about the signal. Trend estimation is not only useful for trends but also could yield seasonality(cycles) of data as well. I will introduce various ways to detect trends in time series signals. With more and more sensors readily available and collection of data becomes more ubiquitous and enables machine to machine communication(a.k.a internet of things), time series signals play more and more important role in both data collection process and also naturally in the data analysis. Data aggregation from different sources and from many people make time-series analysis crucially important in these settings. Detecting trends and patterns in time-series signals enable people to respond these changes and take actions intelligibly. Historically, trend estimation has been useful in macroeconomics, financial time series analysis, revenue management and many more fields to reveal underlying trends from the time series signals. Trend estimation is a family of methods to be able to detect and predict tendencies and regularities in time series signals without knowing any information a priori about the signal. Trend estimation is not only useful for trends but also could yield seasonality(cycles) of data as well. Robust estimation of increasing and decreasing trends not only infer useful information from the signal but also prepares us to take actions accordingly and more intelligibly where the time of response and to action is important. In this talk, I will introduce following trend estimation methods and compare them in real-world datasets comparing their advantages and disadvantages of each algorithm: - Moving average filtering - Exponential smoothing, - Median filtering, - Bandpass filtering, - Hodrick Prescott Filter, - Gradient Boosting Regressor, - l_1 trend filtering(my own library) Materials Available Slides: http://bugra.github.io/pages/deck/2015-07-25/#/ Github Repo: https://github.com/bugra/pydata-seattle-2015 Notebook Link: https://github.com/bugra/pydata-seattle-2015/blob/master/notebooks/Trend%20Estimation%20Methods.ipynb
Views: 3364 PyData
Signal Processing and Machine Learning Techniques for Sensor Data Analytics
 
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Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe ------------------------------------------------------------------------- An increasing number of applications require the joint use of signal processing and machine learning techniques on time series and sensor data. MATLAB can accelerate the development of data analytics and sensor processing systems by providing a full range of modelling and design capabilities within a single environment. In this webinar we present an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone. We introduce common signal processing methods in MATLAB (including digital filtering and frequency-domain analysis) that help extract descripting features from raw waveforms, and we show how parallel computing can accelerate the processing of large datasets. We then discuss how to explore and test different classification algorithms (such as decision trees, support vector machines, or neural networks) both programmatically and interactively. Finally, we demonstrate the use of automatic C/C++ code generation from MATLAB to deploy a streaming classification algorithm for embedded sensor analytics.
Views: 11891 MATLAB
Using SAX For Anomaly Detection
 
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Ray Richardson, Simularity CTO, explains in three minutes how to use Symbolic Aggregate approXimation (SAX) for anomaly detection in time series data at the 2015 MLConf New York. For details and a free consultation contact us at http://www.simularity.com
Views: 1726 Simularity
Jeffrey Yau - Time Series Forecasting using Statistical and Machine Learning Models
 
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PyData New York City 2017 Time series data is ubiquitous, and time series modeling techniques are data scientists’ essential tools. This presentation compares Vector Autoregressive (VAR) model, which is one of the most important class of multivariate time series statistical models, and neural network-based techniques, which has received a lot of attention in the data science community in the past few years.
Views: 20313 PyData
How to Use Tensorflow for Time Series (Live)
 
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We're going to use Tensorflow to predict the next event in a time series dataset. This can be applied to any kind of sequential data. Code for this video: https://github.com/llSourcell/rnn_tutorial Please Subscribe! And Like. And comment. That's what keeps me going. More learning resources: https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data https://www.reddit.com/r/MachineLearning/comments/4ervmf/tensorflow_rnn_time_series_prediction/ https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/ http://nbviewer.jupyter.org/github/jsseely/tensorflow-rnn-tutorial/blob/master/TensorFlow%20RNN%20tutorial.ipynb Join us in the Wizards slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 54577 Siraj Raval
Bugra Akyildiz - Outlier Detection in Time Series Signals
 
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PyData SV 2014 Many real-world datasets have missing observations, noise and outliers; usually due to logistical problems, component failures and erroneous procedures during the data collection process. Although it is easy to avoid missing points and noise to some level, it is not easy to detect wrong measurements and outliers in the dataset. These outliers may present a larger problem in time-series signals since every data point has a temporal dependency to the data point before and after. Therefore, it is crucially important to be able to detect and possibly correct these outliers. In this talk, I will introduce three different methods to be able to detect outliers in time-series signals; Fast Fourier Transform(FFT), Median Filtering and Bayesian approach. http://bugra.github.io/work/notes/2014-03-31/outlier-detection-in-time-series-signals-fft-median-filtering/
Views: 3306 PyData
Robust anomaly detection for real user monitoring data - Velocity 2016, Santa Clara, CA
 
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Code: https://github.com/linkedin/luminol For the past year, LinkedIn has been running and iteratively improving Luminol, its anomaly detection system that identifies anomalies in real user monitoring (RUM) data for LinkedIn pages and apps. Ritesh Maheshwari and Yang Yang offer an overview of Luminol, focusing on how to build a low-cost end-to-end system that can leverage any algorithm, and explain lessons learned and best practices that will be useful to any engineering or operations team. LinkedIn will be open sourcing its Python library for anomaly detection and correlation during the talk. Topics include: Use cases How to avoid an alert black hole Data processing Overview of Luminol Root cause detection Alerting Success stories
Views: 4003 Ritesh Maheshwari
Andreas Kopecky: Implementation of anomaly detection in time series network data (@rubyslava #50)
 
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To detect anomalies in network traffic and thus possible security issues it is essential to differentiate between normal and abnormal states. Security events are counted and analyzed for that reason but are changing in numbers with regard to business hours, weekends, etc. The statistical model to solve the challenge is presented including visual representation of the analyses as well as the implementation in PyMC3. Furthermore the reasons for not being able to use Ruby in that case are highlighted. http://lanyrd.com/2015/rubyslava-june/sdpkpm/
Views: 212 Rubyslava
Outlier Detection/Removal Algorithm
 
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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 13134 Udacity
Outlier Analysis/Detection with Univariate Methods Using Tukey boxplots in Python - Tutorial 20
 
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In this Tutorial, You will learn how to do outlier analysis using uni-variate methods for Extreme Value analysis. You will learn about identifying outliers using from Tukey boxplots and Applying Tukey outlier labeling. This is the 20th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We'll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets. Download Link for Cars Data Set: https://www.4shared.com/s/fWRwKoPDaei Download Link for Enrollment Forecast: https://www.4shared.com/s/fz7QqHUivca Download Link for Iris Data Set: https://www.4shared.com/s/f2LIihSMUei https://www.4shared.com/s/fpnGCDSl0ei Download Link for Snow Inventory: https://www.4shared.com/s/fjUlUogqqei Download Link for Super Store Sales: https://www.4shared.com/s/f58VakVuFca Download Link for States: https://www.4shared.com/s/fvepo3gOAei Download Link for Spam-base Data Base: https://www.4shared.com/s/fq6ImfShUca Download Link for Parsed Data: https://www.4shared.com/s/fFVxFjzm_ca Download Link for HTML File: https://www.4shared.com/s/ftPVgKp2Lca
Views: 6718 TheEngineeringWorld
Seeing Behaviors as Humans Do׃ Uncovering Hidden Patterns in Time Series Data w⁄ Deep Networks
 
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Time-series (longitudinal) data occurs in nearly every aspect of our lives; including customer activity on a website, financial transactions, sensor/IoT data. Just like in written text, specific events in a sequence of events are affected by the past and affect events in the future, and this can reveal a lot of hidden structure in the source of the events. Yet, today's predictive techniques largely rely on demographic (cross-sectional) data and do not take into account the sequences of events as they occur. In this session, Mohammad will discuss techniques for taking time-series data from a variety of domains and sources and grouping entities based on temporal behavior, using RNNs. These clusters of time-series sequences can either be visualized or used for campaign targeting in the case of user clickstream behavior or understanding stock symbols that behave similarly based on their trading behavior. About the Speaker: Mohammad Saffar is a deep learning software engineer at Arimo, world's leader in AI platform for the Enterprise. He loves being involved in designing and implementing real-world systems specifically machine learning and data mining related systems. His past projects involve video-based intent recognition, multi-agent intent recognition and face recognition with deep networks. Mohammad holds a PhD. in Computer Science from the University of Nevada-Reno. *This talk was at the Cloudera Wrangle 2016*
Views: 2135 Arimo, Inc.
Outlier Detection
 
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Access the Outlier Detection Workshop materials here: https://rapidminer-my.sharepoint.com/:f:/p/hmatusow/Eo1pCY2pIZdKvi8eX9Zs2ksBBLKxL5EmruRznwLzRR4TWQ?e=9lAtkL
Views: 222 RapidMiner, Inc.
#bbuzz 2015: Andrew Clegg - Signatures, patterns and trends: Timeseries data mining at Etsy
 
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Find more information here: http://berlinbuzzwords.de/session/signatures-patterns-and-trends-timeseries-data-mining-etsy Etsy loves metrics. Everything that happens in our data centres gets recorded, graphed and stored. But with over a million metrics flowing in constantly, it’s hard for any team to keep on top of all that information. Graphing everything doesn’t scale, and traditional alerting methods based on thresholds become very prone to false positives. That’s why we started Kale, an open-source software suite for pattern mining and anomaly detection in operational data streams. These are big topics with decades of research, but many of the methods in the literature are ineffective on terabytes of noisy data with unusual statistical characteristics, and techniques that require extensive manual analysis are unsuitable when your ops teams have service levels to maintain. In this talk I’ll briefly cover the main challenges that traditional statistical methods face in this environment, and introduce some pragmatic alternatives that scale well and are easy to implement (and automate) on Elasticsearch and similar platforms. I’ll talk about the stumbling blocks we encountered with the first release of Kale, and the resulting architectural changes coming in version 2.0. And I’ll go into a little technical detail on the algorithms we use for fingerprinting and searching metrics, and detecting different kinds of unusual activity. These techniques have potential applications in clustering, outlier detection, similarity search and supervised learning, and they are not limited to the data centre but can be applied to any high-volume timeseries data. Kale version 1 is described here: https://codeascraft.com/2013/06/11/introducing-kale/ Version 2 has the same goals but a very different architecture and suite of tools. Come along if you'd like to learn more.
Time Series Prediction
 
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Time series is the fastest growing category of data out there! It's a series of data points indexed in time order. Often, a time series is a sequence taken at successive equally spaced points in time. In this video, I'll cover 8 different time series techniques that will help us predict the price of gold over a period of 3 years. We'll compare the results of each technique, and even consider using a learning technique. From Holts Winter Method to Vector Auto Regression to Reinforcement Learning, we've got a lot to cover here. Enjoy! Code for this video: https://github.com/llSourcell/Time_Series_Prediction Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://www.altumintelligence.com/articles/a/Time-Series-Prediction-Using-LSTM-Deep-Neural-Networks https://blog.statsbot.co/time-series-prediction-using-recurrent-neural-networks-lstms-807fa6ca7f https://towardsdatascience.com/bitcoin-price-prediction-using-time-series-forecasting-9f468f7174d3 https://www.datascience.com/blog/time-series-forecasting-machine-learning-differences https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/ https://www.youtube.com/watch?v=hhJIztWR_vo Join us at School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: www.theschool.ai/jobs/ Need help on a project? See our consulting group: www.theschool.ai/consulting-group/
Views: 21250 Siraj Raval
TensorFlow Tutorial #23 Time-Series Prediction
 
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How to predict time-series data using a Recurrent Neural Network (GRU / LSTM) in TensorFlow and Keras. Demonstrated on weather-data. https://github.com/Hvass-Labs/TensorFlow-Tutorials
Views: 30812 Hvass Laboratories
Details of Anomaly Detection in Big Data, Nikunj Oza, 20140728
 
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Nikunj Oza, Leader of the Data Sciences Group, NASA Ames Research Center Joint Event with Hadoop Talks Meetup Data-driven methods for anomaly detection identifies as anomalies those data points that do not fit with most of the data in some sense. For example, the anomalies may have greater distances to their nearest neighbors or lower probabilities with respect to an appropriate probability model. However, measuring distances between points or probabilities of points is problematic when working with "big data," with their heterogeneity and volume. In this talk, I will describe the problem in more detail, the heterogeneous data sources available to us, the methods we use to leverage these data sources, and the general data management and data mining problems that we need to solve moving forward. Speaker Bio Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads the Discovery of Precursors to Safety Incidents (DPSI) team which applies data mining to aviation safety. Dr. Oza’s 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His DPSI team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administrator’s Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley. http://www.meetup.com/SF-Bay-ACM/events/183069232/ http://www.sfbayacm.org/event/hadoop-talk-details-anomaly-detection-big-data
Views: 1409 San Francisco Bay ACM
Intro to Kapacitor for Time Series Anomaly Detection | Getting Started 5 of 7
 
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Get an overview of Kapacitor, InfluxDB’s native data processing engine. The session will cover how to install, configure and build custom TICK scripts enable alerting and time series anomaly detection. Learn more or download Kapacitor now: https://www.influxdata.com/time-series-platform/kapacitor/ In this webinar you will learn: - The Kapacitor Computational Model - Understand the TICK Script Syntax - Run a Kapacitor Instance - Create a TICK Script - Basic Review of Kapacitor's User Defined Functions (UDFs)
Views: 4344 InfluxData
Real-time anomaly detection system for time series at scale
 
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Author: Meir Toledano, Anodot, Ltd. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 336 KDD2017 video
Lecture 15.1 — Anomaly Detection Problem | Motivation  — [ Machine Learning | Andrew Ng ]
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Weka Tutorial 19: Outliers and Extreme Values (Data Preprocessing)
 
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This tutorial shows how to detect and remove outliers and extreme values from datasets using WEKA.
Views: 31711 Rushdi Shams
Nikunj Oza: "Data-driven Anomaly Detection" | Talks at Google
 
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This talk will describe recent work by the NASA Data Sciences Group on data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, and New York. This data mining approach is designed to discover operationally significant flight anomalies, which were not pre-defined. These methods are complementary to traditional exceedance-based methods, in that they are more likely to yield false alarms, but they are also more likely to find previously-unknown anomalies. We discuss the discoveries that our algorithms have made that exceedance-based methods did not identify. Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team which applies data mining to aviation safety. Dr. Ozaąs 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administratorąs Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.
Views: 7691 Talks at Google
Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough
 
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In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors. For additional resources on anomaly detection and on streaming data: Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Streaming Platforms: A Microservices Advantage” https://www.mapr.com/blog/key-requirements-streaming-platforms-micro-services-advantage-whiteboard-walkthrough-part-1 Read technical blog/tutorial “Getting Started with MapR Streams” sample programs by Tugdual Grall https://www.mapr.com/blog/getting-started-sample-programs-mapr-streams Download free pdf for the book Introduction to Apache Flink by Ellen Friedman and Ted Dunning https://www.mapr.com/introduction-to-apache-flink
Views: 4321 MapR Technologies
Outliers Detection in Time Series w Cassandra & Spark (Jean Armel Luce, Orange) | C* Summit 2016
 
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An outlier in time series data is often a signal that must be addressed. Domains where outliers detection can give noteworthy informations are various: -Technical supervision -Cybersecurity, fraud detection -KPI business -. . . At Orange, we developed Astrolog to detect outliers from our time series data and analyze unexpected behaviors. Linked to Astrolog, Astropolis can trigger different levels of reactions according to the situation, such as log anomalies, send alerts by mail, stop some processes, .. During the past months, Astrolog and Astropolis helped our users to detect early some discrepancies and trigger very quickly the right reaction avoiding potential dramatic consequences Spark and Cassandra are capable of handling the challenges that might arise for this use case : massive scalability, high availability and high performance. In this session, I will show how we are using Cassandra and Spark for analyzing time series data and consequently trigger the right actions About the Speaker Jean Armel Luce Tech Lead, Orange Jean Armel is a Database Tech Lead at Orange, with more than 20 years of software development in various environments. For the last 5 years, he has been using Cassandra for many applications that require scalability and high availability. Jean Armel has worked with Apache Cassandra since 2011 and is a regular speaker at technical conferences in France, London or San Francisco.
Views: 1187 DataStax
Real Time Sensor Anomaly Detection with Sci Kit Learn and the Azure Stack - Ari Bornstein
 
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This talk was presented on Pycon Israel 2017. http://il.pycon.org/2017/ https://twitter.com/pyconil https://www.facebook.com/pyconisrael/
Views: 1546 PyCon Israel
Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques
 
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Machine learning techniques used in network intrusion detection are susceptible to “model poisoning” by attackers. The speaker will dissect this attack, analyze some proposals for how to circumvent such attacks, and then consider specific use cases of how machine learning and anomaly detection can be used in the web security context. Author: Clarence Chio More: http://www.phdays.com/program/tech/40866/
Views: 9368 Positive Technologies
Data transformations and time series modeling with R and Azure ML
 
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This tutorial video illustrates how to perform some basic data transformations and time series modeling using R and Microsoft's Azure Machine Learning. The video complements the Quick Start Guide to R in Azure ML at http://azure.microsoft.com/en-gb/documentation/articles/machine-learning-r-quickstart/
Views: 8538 Stephen Elston
Dafne van Kuppevelt | Deep learning for time series made easy
 
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PyData Amsterdam 2017 Deep learning is a state of the art method for many tasks, such as image classification and object detection. For researchers that have time series data, but are not an expert on deep learning, the barrier can be high to start using deep learning. We developed mcfly, an open source python library, to help machine learning novices explore the value of deep learning for time series data. In this talk, we will explore how machine learning novices can be aided in the use of deep learning for time series classification. In a variety of scientific fields researchers face the challenge of time series classification. For example, to classify activity types from wrist-worn accelerometer data or to classify epilepsy from electroencephalogram (EEG) data. For researchers who are new to the field of deep learning, the barrier can be high to start using deep learning. In contrast to computer vision use cases, where there are tools such as caffe that provide pre-defined models to apply on new data, it takes some knowledge to choose an architecture and hyperparameters for the model when working with time series data. We developed mcfly, an open source python library to make time series classification with deep learning easy. It is a wrapper around Keras, a popular python library for deep learning. Mcfly provides a set of suitable architectures to start with, and performs a search over possible hyper-parameters to propose a most suitable model for the classification task provided. We will demonstrate mcfly with excerpts from (multi-channel) time series data from movement sensors that are associated with a class label, namely activity type (sleeping, walking, climbing stairs). In our example, mcfly will be used to train a deep learning model to label new data.
Views: 10217 PyData
Anomaly Detection with PCA in R
 
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This vlog utilizes the power of PCA to build a machine learning model to perform anomaly detection.
Views: 2265 Keshav Singh
A Framework for Periodic Outlier Pattern Detection in Time-Series Sequences
 
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A Framework for Periodic Outlier Pattern Detection in Time-Series Sequences: Abstract: Periodic pattern detection in time-ordered sequences is an important data mining task, which discovers in the time series all patterns that exhibit temporal regularities. Periodic pattern mining has a large number of applications in real life; it helps understanding the regular trend of the data along time, and enables the forecast and prediction of future events. An interesting related and vital problem that has not received enough attention is to discover outlier periodic patterns in a time series.Outlier patterns are defined as those which are different from the rest of the patterns; outliers are not noise. While noise does not belong to the data and it is mostly eliminated by preprocessing, outliers are actual instances in the data but have exceptional characteristics compared with the majority of the other instances. Outliers are unusual patterns that rarely occur, and, thus, have lesser support (frequency of appearance) in the data. Outlier patterns may hint toward discrepancy in the data such as fraudulent transactions, network intrusion, change in customer behavior, recession in the economy, epidemic and disease biomarkers, severe weather conditions like tornados, etc. We argue that detecting the periodicity of outlier patterns might be more important in many sequences than the periodicity of regular, more frequent patterns. In this paper, we present a robust and time efficient suffix tree-based algorithm capable of detecting the periodicity of outlier patterns in a time series by giving more significance to less frequent yet periodic patterns. Several experiments have been conducted using both real and synthetic data; all aspects of the proposed approach are comparedwith the existing algorithm InfoMiner; the reported results demonstrate the effectiveness and applicability of the proposed approach.
Views: 217 Prosys System
Anomaly Detection 101 - Elizabeth (Betsy) Nichols Ph.D.
 
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This presentation surveys a collection of techniques for detecting anomalies in a DevOps environment. Each of the techniques has strengths and weaknesses that are illustrated via real-world (anonymized) customer data. Techniques discussed include deterministic and statistical models as well as uni-variate and multi-variate analytics. Examples are given that show concrete evidence where each can succeed and each can fail. This presentation is about concepts and how to think about alternative anomaly detection techniques. This presentation is not an academic discourse in math, statistics or probability theory. Elizabeth A. Nichols (Betsy) is Chief Data Scientist at Netuitive, Inc. In this role she is responsible for leading the company's vision and technologies for analytics, modeling, and algorithms. Betsy has applied mathematics and computer technologies to create systems for war gaming, space craft mission optimization, industrial process control, supply chain logistics, electronic trading, advertising networks, IT security and risk models, and network and systems management. She has co-founded three companies, all of which delivered analytics to commercial and government enterprises. Betsy graduated with an A.B. from Vassar College and a Ph.D. in Mathematics from Duke University. Check her out on LinkedIn (https://www.linkedin.com/in/elizabethanichols) for more information.
Machine Learning Tutorial 15 - Outliers
 
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Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics). Machine Learning and Predictive Analytics. #MachineLearning One of the processes in machine learning is data cleaning. This video deals specifically with the problems that outliers cause. They mess up our data visualization and our measures of central tendency. This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://bit.ly/JoinCCNewsletter Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 1015 Caleb Curry
Unsupervised Anomaly Detection With Advanced Analytics: Your Next Steps - Harizo Rajaona
 
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How can we improve anomaly detection with unsupervised methods? After a quick sum up of supervised methods, we'll show how advanced machine learning can leverage the power of algorithms. We'll conclude with a few use case applications from different customers. #HyperightDataTalks is a video podcast of best presentations, discussions and interviews with some of the most innovative minds, enterprise practitioners, technology and service providers, start-ups and academics, working with Data Science, Data Management, Big Data, Analytics, AI, IOT and much more. All presentations are taken from Hyperight´s Data summits and now available for you. For more interviews, audio podcast and videos from some of the best presentations from our Data Summits, please visit http://www.hyperight.com Presentation recorded during: Nordic Data Science and Machine Learning Summit 2017 - http://www.nordicdatasciencesummit.com/ Follow us on Twitter: https://Twitter.com/datasweden More information about Hyperight: http://www.hyperight.com/ Subscribe to our channel: https://www.youtube.com/channel/UCCLYBm1MHI3jIvZo9YKPq-g
Views: 809 Hyperight AB
NEW - Fraud and Anomaly Detection using Oracle Advanced Analytics Part 1 Concepts
 
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This is Part 1 of my Fraud and Anomaly Detection using Oracle Advanced Analytics presentations and demos series. Hope you enjoy! www.twitter.com/CharlieDataMine
Views: 5894 Charles Berger
Recurrent Neural Networks (RNN / LSTM )with Keras - Python
 
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In this tutorial, we learn about Recurrent Neural Networks (LSTM and RNN). Recurrent neural Networks or RNNs have been very successful and popular in time series data predictions. There are several applications of RNN. It can be used for stock market predictions , weather predictions , word suggestions etc. SimpleRNN , LSTM , GRU are some classes in keras which can be used to implement these RNNs. The backend can be Theano as well as TensorFlow. Find the codes here GitHub : https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Good Reads : http://karpathy.github.io/ Recommended book for Deep Learning : http://amzn.to/2nXweQS
Views: 55265 The SemiColon

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