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Time Series Data Analysis with pandas
 
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Wes McKinney In this tutorial, I'll give a brief overview of pandas basics for new users, then dive into the nuts of bolts of manipulating time series data in memory. This includes such common topics date arithmetic, alignment and join / merge method
Views: 50566 Next Day Video
Excel - Time Series Forecasting - Part 1 of 3
 
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Part 2: http://www.youtube.com/watch?v=5C012eMSeIU&feature=youtu.be Part 3: http://www.youtube.com/watch?v=kcfiu-f88JQ&feature=youtu.be This is Part 1 of a 3 part "Time Series Forecasting in Excel" video lecture. Be sure to watch Parts 2 and 3 upon completing Part 1. The links for 2 and 3 are in the video as well as above.
Views: 725332 Jalayer Academy
Time Series  Analysis Theory & Uni-variate Forecasting Techniques
 
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Time Series analysis is the analysis of uni-variate time varying data which is used to predict future values of a certain variable. In this video, you will learn about what are time series, cross section and Panel data sets, what are univariate and multi variate time series, what is stationarity, what is a white noise process etc. You will Learn about AR, MA, ARMA and ARIMA models. You will learn about building an ARIMA model using Box-Jenkins method. ANalytics Study Pack : http://analyticuniversity.com/ Contact : [email protected] Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx
Lecture - 35 The Analysis of Time Series
 
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Lecture series on Project and Production Management by Prof. Arun kanda, Department of Mechanical Engineering, IIT Delhi. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 138750 nptelhrd
Time Series Forecasting Using Statistical and Machine Learning Models || Jeffrey Yau
 
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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. EVENT: PyData New York City 2017 PERMISSIONS: PyData Organizer provided Coding Tech with the permission to republish this video. CREDITS: Original video source: https://www.youtube.com/watch?v=_vQ0W_qXMxk
Views: 2712 Coding Tech
Time Travel in Fiction Rundown
 
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Thanks to YouTube RED’s new original series, LIFELINE, for sponsoring this video. Watch the first episode for free: https://www.youtube.com/watch?v=Ru4zkxNuJ_I And thanks to my friends Sam and Niko (and all the rest) at the Corridor Digital channel for making awesome videos. Support MinutePhysics on Patreon! http://www.patreon.com/minutephysics Link to Patreon Supporters: http://www.minutephysics.com/supporters/ For ages I’ve been thinking about doing a video analyzing time travel in fiction and doing a comparison of different fictional time travels – some do use wormholes, some relativistic/faster than light travel with time dilation, some closed timelike curves, some have essentially “magic” or no consistent rules that make any sense, or TARDIS's, or whatever. This video is an explanation of how time travel functions in different popular movies, books, & shows – not how it works “under the hood", but how it causally affects the perspective of characters’ timelines (who has free will? can you change things by going back to the past or forwards into the future?). In particular, I explain Ender's Game, Planet of the Apes, Harry Potter and the Prisoner of Azkaban, Primer, Bill & Ted’s Excellent Adventure, Back to the Future, Groundhog Day, Looper, the video game “Braid”, and Lifeline. MinutePhysics is on twitter - @minutephysics And facebook - http://facebook.com/minutephysics And Google+ (does anyone use this any more?) - http://bit.ly/qzEwc6 Minute Physics provides an energetic and entertaining view of old and new problems in physics -- all in a minute! Created by Henry Reich
Views: 3631939 minutephysics
The Big Bang Theory - Analysis Paralysis
 
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"Analysis Paralysis" at its finest... it makes me laugh every single time. This clip is obviously property of CBS
Views: 38099 rystrm
How to Predict Stock Prices Easily - Intro to Deep Learning #7
 
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We're going to predict the closing price of the S&P 500 using a special type of recurrent neural network called an LSTM network. I'll explain why we use recurrent nets for time series data, and why LSTMs boost our network's memory power. Coding challenge for this video: https://github.com/llSourcell/How-to-Predict-Stock-Prices-Easily-Demo Vishal's winning code: https://github.com/erilyth/DeepLearning-SirajologyChallenges/tree/master/Image_Classifier Jie's runner up code: https://github.com/jiexunsee/Simple-Inception-Transfer-Learning More Learning Resources: http://colah.github.io/posts/2015-08-Understanding-LSTMs/ http://deeplearning.net/tutorial/lstm.html https://deeplearning4j.org/lstm.html https://www.tensorflow.org/tutorials/recurrent http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ https://blog.terminal.com/demistifying-long-short-term-memory-lstm-recurrent-neural-networks/ Please subscribe! And like. And comment. That's what keeps me going. Join other Wizards in our Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 music in the intro is chambermaid swing by parov stelar 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/
Views: 407670 Siraj Raval
Understanding Wavelets, Part 1: What Are Wavelets
 
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This introductory video covers what wavelets are and how you can use them to explore your data in MATLAB®. •Try Wavelet Toolbox: https://goo.gl/m0ms9d •Ready to Buy: https://goo.gl/sMfoDr The video focuses on two important wavelet transform concepts: scaling and shifting. The concepts can be applied to 2D data such as images. Video Transcript: Hello, everyone. In this introductory session, I will cover some basic wavelet concepts. I will be primarily using a 1-D example, but the same concepts can be applied to images, as well. First, let's review what a wavelet is. Real world data or signals frequently exhibit slowly changing trends or oscillations punctuated with transients. On the other hand, images have smooth regions interrupted by edges or abrupt changes in contrast. These abrupt changes are often the most interesting parts of the data, both perceptually and in terms of the information they provide. The Fourier transform is a powerful tool for data analysis. However, it does not represent abrupt changes efficiently. The reason for this is that the Fourier transform represents data as sum of sine waves, which are not localized in time or space. These sine waves oscillate forever. Therefore, to accurately analyze signals and images that have abrupt changes, we need to use a new class of functions that are well localized in time and frequency: This brings us to the topic of Wavelets. A wavelet is a rapidly decaying, wave-like oscillation that has zero mean. Unlike sinusoids, which extend to infinity, a wavelet exists for a finite duration. Wavelets come in different sizes and shapes. Here are some of the well-known ones. The availability of a wide range of wavelets is a key strength of wavelet analysis. To choose the right wavelet, you'll need to consider the application you'll use it for. We will discuss this in more detail in a subsequent session. For now, let's focus on two important wavelet transform concepts: scaling and shifting. Let' start with scaling. Say you have a signal PSI(t). Scaling refers to the process of stretching or shrinking the signal in time, which can be expressed using this equation [on screen]. S is the scaling factor, which is a positive value and corresponds to how much a signal is scaled in time. The scale factor is inversely proportional to frequency. For example, scaling a sine wave by 2 results in reducing its original frequency by half or by an octave. For a wavelet, there is a reciprocal relationship between scale and frequency with a constant of proportionality. This constant of proportionality is called the "center frequency" of the wavelet. This is because, unlike the sinewave, the wavelet has a band pass characteristic in the frequency domain. Mathematically, the equivalent frequency is defined using this equation [on screen], where Cf is center frequency of the wavelet, s is the wavelet scale, and delta t is the sampling interval. Therefore when you scale a wavelet by a factor of 2, it results in reducing the equivalent frequency by an octave. For instance, here is how a sym4 wavelet with center frequency 0.71 Hz corresponds to a sine wave of same frequency. A larger scale factor results in a stretched wavelet, which corresponds to a lower frequency. A smaller scale factor results in a shrunken wavelet, which corresponds to a high frequency. A stretched wavelet helps in capturing the slowly varying changes in a signal while a compressed wavelet helps in capturing abrupt changes. You can construct different scales that inversely correspond the equivalent frequencies, as mentioned earlier. Next, we'll discuss shifting. Shifting a wavelet simply means delaying or advancing the onset of the wavelet along the length of the signal. A shifted wavelet represented using this notation [on screen] means that the wavelet is shifted and centered at k. We need to shift the wavelet to align with the feature we are looking for in a signal.The two major transforms in wavelet analysis are Continuous and Discrete Wavelet Transforms. These transforms differ based on how the wavelets are scaled and shifted. More on this in the next session. But for now, you've got the basic concepts behind wavelets.
Views: 127876 MATLAB
Introduction to Big O Notation and Time Complexity (Data Structures & Algorithms #7)
 
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Big O notation and time complexity, explained. Check out Brilliant.org (https://brilliant.org/CSDojo/), a website for learning math and computer science concepts through solving problems. First 200 subscribers will get 20% off through the link above. Special thanks to Brilliant for sponsoring this video. This was #7 of my data structures & algorithms series. You can find the entire series in a playlist here: https://goo.gl/wy3CWF Also, keep in touch on Facebook: https://www.facebook.com/entercsdojo
Views: 106876 CS Dojo
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: 3209 PyData
Webinar | Time Series Forecasting and It's Application in the Technology Sector
 
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Webinar with Jyotirmoy Patra on "Time Series Forecasting and Its Application in Technology Sector". In the present business world forecasting is an arm of analytics which can provide a powerful toolkit for analyzing time series data and make better decisions. About the Speaker Jyotirmoy Patra is working as a Data Scientist at IBM with more than 8 years of extensive experience in the field of analytics and research. He has provided solutions in a diverse set of domains like Marketing - CPG & Healthcare, Resource Planning , Manufacturing. His recent work has been in the field of Demand planning using forecasting tools, Time Series Clustering and Ignite defect prediction.
Views: 237 Great Learning
Standardization Fundamentals - Time Series Analysis
 
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Provides an overview of the time series infrastructure provided by Modano to assist in efficiently creating standardized, best practice time series-based spreadsheets, including time series assumptions, lookup tables and sheets.
Views: 121 Modano
Adventure Time's Finale EXPLAINED! (Easter Eggs, Lore, & Analysis!)
 
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Adventure Time has officially reached its epic finale, and boy was there a lot to unpack in that 44 minute episode. Whether you’re an old fan who was left confused by some newer plot threads, or a die hard Adventure Time expert making sure you caught all the Easter eggs, NerdWire is here for all your post-finale episode dissection needs. I’m Kris Carr and this is the Adventure Time Finale explained. #AdventureTime #Golb #AdventureTimeFinale Also make sure to check us out on Roku: https://channelstore.roku.com/details/233248/nerdwire Subscribe For More Obsev Now! ►► http://bit.ly/SubToOBSEV ►► ►► Check out our site: http://www.obsev.com Like us on Facebook: http://facebook.com/obsev Follow us on Twitter: http://twitter.com/obsevstudios Find us on Instagram: http://instagram.com/obsev
Views: 3569497 NerdWire
Univariate data analysis - 14 - The paired test: theory and an example
 
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These videos are part of the FREE online book, "Process Improvement using Data", http://yint.org/pid Related is the Coursera course, "Experimentation for Improvement". Join the course for FREE at http://yint.org/experiment
Views: 974 Kevin Dunn
Easy Econometics series: Time Series Cross section and panel data HINDI/URDU
 
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what is the difference between cross section and panel data??? HERE ARE THE LINKS FOR MY ALL VIDEOS YOU MAY LIKE  Links for; MOTIVATIONAL videos (40 + videos) https://www.youtube.com/watch?v=WM3Upd0MeDg&index=19&list=PLU-cxjF-s0HmnD_TYqmkb6VBL6AusSB_Y RESEARCH & THESIS Writing (6 videos) https://www.youtube.com/watch?v=ib7yYggOgpA&t=308s&list=PLU-cxjF-s0HlVJnf2dev1KtoAD3ZljWVg&index=3 OLIGOPOLY & GAME THEORY ( 6 videos) https://www.youtube.com/playlist?list=PLU-cxjF-s0HnlCialpcyfz0O8xminDXja Basics of ECONOMICS; Micro & Macro (25 + videos) https://www.youtube.com/playlist?list=PLU-cxjF-s0HngkQ9xdQ5waYtpEO2MkP-D ECONOMETRICS; An introduction (20 + videos) https://www.youtube.com/watch?v=WNf1DLTRlTo&index=7&list=PLU-cxjF-s0Hk0DOXPqauiHBfRgaFpqYxK&t=0s Principles of MARKETING ( 10+ videos) https://www.youtube.com/watch?v=mCAogfEz6bs&t=0s&list=PLU-cxjF-s0HlgTwSl9Xk3bx5z8q7kOD6F&index=12 Management; HRM & STRATEGIC MANAGEMENT ( 6 videos) https://www.youtube.com/watch?v=uO3azOb2icE&t=314s&list=PLU-cxjF-s0Hml7o706eKofZ7D4bXIcJwH&index=6 ========= About KOKAB MANZOOR ========= Kokab Manzoor is Certified Trainer, Speaker & Career Counsellor. He has trained thousands of students & Professionals about Leadership & Management skills, Motivation, Personality Grooming, Career selection and about variety of other life skills. Has a sound understanding of needed traits for workplace success and a strong ability to train employees in improving those characteristics. Follow me www.Youtube.com/kokabmanzoor www.Facebook.com/kokabmanzoor19 [email protected] http://kokabmanzoor1.blogspot.com/
Views: 1599 Kokab Manzoor
Bayesian Dynamic Modeling: Sharing Information Across Time and Space
 
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This talk will highlight some of the benefits and challenges associated with harnessing the temporal structure present in many datasets. The focus is on Bayesian dynamic modeling approaches, and in particular, the idea of sharing information across time and "space," where space generically refers to the dimensions of the time series. Emily Fox, UW Assistant Professor of Statistics, discusses how to exploit nonparametric and hierarchical models to capture repeated patterns in time and similar structure in space, enabling the modeling of complex and high-dimensional time series. Applications of such approaches are quite diverse, and she demonstrate this by touching upon work in the tasks of speaker diarization, analyzing human motion, detecting changes in volatility of stock indices, parsing EEG, word classification from MEG, and predicting rates of violent crimes in DC and influenza rates in the US.
Views: 16113 UWTV
Fourier Series Part 1
 
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Joseph Fourier developed a method for modeling any function with a combination of sine and cosine functions. You can graph this with your calculator easily and watch the modeling in action. Make sure you're in radian mode and let c=1: f(x) = 4/(pi)*sin(x) + 4/(3pi)*sin(3x) + 4/(5pi)*sin(5x) + 4/(7pi)*sin(7x) + 4/(9pi)*sin(9x) + 4/(11pi)*sin(11x)
Views: 818273 Saul Rémi
Student's t-test
 
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Excel file: https://dl.dropboxusercontent.com/u/561402/TTEST.xls In this video Paul Andersen explains how to run the student's t-test on a set of data. He starts by explaining conceptually how a t-value can be used to determine the statistical difference between two samples. He then shows you how to use a t-test to test the null hypothesis. He finally gives you a separate data set that can be used to practice running the test. Do you speak another language? Help me translate my videos: http://www.bozemanscience.com/translations/ Music Attribution Intro Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License Outro Title: String Theory Artist: Herman Jolly http://sunsetvalley.bandcamp.com/track/string-theory All of the images are licensed under creative commons and public domain licensing: 1.3.6.7.2. Critical Values of the Student’s-t Distribution. (n.d.). Retrieved April 12, 2016, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm File:Hordeum-barley.jpg - Wikimedia Commons. (n.d.). Retrieved April 11, 2016, from https://commons.wikimedia.org/wiki/File:Hordeum-barley.jpg Keinänen, S. (2005). English: Guinness for strenght. Retrieved from https://commons.wikimedia.org/wiki/File:Guinness.jpg Kirton, L. (2007). English: Footpath through barley field. A well defined and well used footpath through the fields at Nuthall. Retrieved from https://commons.wikimedia.org/wiki/File:Footpath_through_barley_field_-_geograph.org.uk_-_451384.jpg pl.wikipedia, U. W. on. ([object HTMLTableCellElement]). English: William Sealy Gosset, known as “Student”, British statistician. Picture taken in 1908. Retrieved from https://commons.wikimedia.org/wiki/File:William_Sealy_Gosset.jpg The T-Test. (n.d.). Retrieved April 12, 2016, from http://www.socialresearchmethods.net/kb/stat_t.php
Views: 327107 Bozeman Science
Fourier Transform, Fourier Series, and frequency spectrum
 
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Fourier Series and Fourier Transform with easy to understand 3D animations.
Time Series introduction
 
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Join my Whatsapp Broadcast / Group to receive daily lectures on similar topics through this Whatsapp direct link https://wa.me/917736022001 by simply messaging YOUTUBE LECTURES Did you liked this video lecture? Then please check out the complete course related to this lecture, BASICS OF STATISTICS – A COMPREHENSIVE STUDY with 100+ Lectures, 8+ hours content available at discounted price (10% off)with life time validity and certificate of completion. Enrollment Link For Students Outside India: https://bit.ly/2MspfMr Enrollment Link For Students From India: https://www.instamojo.com/caraja/basics-of-statistics-a-complete-study/?discount=inybosacs2 Our website link : https://www.carajaclasses.com Welcome to the course Basics of Statistics - A Comprehensive Study. Statistics is required in every walk of business life to take decisions. When we take decision, it should be an informed one. Once we have statistics, we can be sure that decision is taken considering various factors. In this course, you will learn about the basics of statistics in depth covering a) Introduction to Statistics; b) Collection of Data; c) Presentation of Data; d) Frequency Distribution; e) Measures of Central Tendency covering Arithmetic Mean, Median and Mode f) Measures of Dispersion covering Range, Quartile Deviation and Standard Deviation g) Correlation h) Regression. This course is basically a bundle of other courses namely i) Basics of Business Statistics ii) Statistics - Measures of Central Tendency iii) Statistics - Measures of Dispersion iv) Statistics and Correlation. If you are buying this course, make sure you don't buy the above courses. This course is structured in self paced learning style. Video lectures are used for delivering the course content. Numerous case studies were solved in hand written presentation. Take this course to gain good knowledge in basics of statistics. What are the requirements? • Students can approach this course with fresh mind. • No prior knowledge in Statistics is required. What am I going to get from this course? • Over 80 lectures and 6.5 hours of content! • Understand Basics of Statistics • Understand Mean, Median and Mode • Understand Deviations like Quartile Deviation, Standard Deviation, etc. • Understand Correlation • Understand Reggression What is the target audience? • CA / CMA / CS Students • Students pursuing CA / CMA / CS / Higher Secondary / Statistics courses • B.Com I Year Students
Views: 157 CARAJACLASSES
1. Introduction to Statistics
 
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*NOTE: This video was recorded in Fall 2017. The rest of the lectures were recorded in Fall 2016, but video of Lecture 1 was not available. MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: https://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet In this lecture, Prof. Rigollet talked about the importance of the mathematical theory behind statistical methods and built a mathematical model to understand the accuracy of the statistical procedure. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Views: 180006 MIT OpenCourseWare
Dows theory | Technical Analysis | Price Action | Basics of stock Market
 
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Dows theory is one of the most important theory in Technical Analysis. Any stock trend analysis is incomplete without dows theory. Broadly, Technical analysis can be categories in two parts. 1) Price Action theory 2) Indicators Price action is about analysis price trend directly. Here, we learn about trend, chart patterns, candlesticks theory, Elliot wave theory, Fibonacci retracement, Gap Analysis and even more. Indicators are about analysis a series of data point which creates an indicator driven from price and volume movement. There are two types of indicators A) Leading indicators B) Lagging Indicators Some of the leading indicators are RSI, Stochastic, SAR, ADX, Williams R. Where as to name some lagging indicators, we can use Moving average, Bollinger band, MACD. The accuracy of lagging indicators are better than leading indicators. You can use price action and indicators both technical analysis for intraday trading. You can watch our complete Free Playlist on Technical analysis course at : https://goo.gl/A44A89 If you are interested in joining our course visit us at: http://www.iplaneducation.com/courses/stock-market/technical-analysis-program/ You can whatapp me for any query: +91-9999616222 Please, Don't forget to like & share this video. Also, subscribe to our channel to watch latest and update videos. It talks a lot about price cycle and trends. So, check it out.
Views: 15122 iPlan Education
NIPS 2015 Workshop (Fox) 15505 Time Series Workshop
 
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Data, in the form of time-dependent sequential observations emerge in many key real-world problems ranging from biological data, to financial markets, to weather forecasting and audio/video processing. However, despite the ubiquity of such data, the vast majority of learning algorithms have been primarily developed for the setting in which sample points are drawn i.i.d. from some possibly unknown fixed distribution. While there exist algorithms designed to handle non-i.i.d. data, these typically assume specific parametric form of data-generating distribution. Such assumptions may undermine the possibly complex nature of modern data which can possess long-range dependency patterns that we now have the computing power to discern. On the other extreme, some online learning algorithms consider a non-stochastic framework without any distributional assumptions. However, such methods may fail to fully address the stochastic aspect of real-world time-series data. lt br gt lt br gt The goal of this workshop is to bring together theoretical and applied researchers interested in the analysis of time series, and the development of new algorithms to process sequential data. This includes algorithms for time series prediction, classification, clustering, anomaly and change point detection, correlation discovery, dimensionality reduction as well as a general theory for learning and comparing stochastic processes. We invite researchers from the related areas of batch and online learning, reinforcement learning, data analysis and statistics, econometrics, and many others to contribute to this workshop.
Views: 341 NIPS
Principal Components Analysis - Georgia Tech - Machine Learning
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-649069103/m-661438544 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 234059 Udacity
But what is the Fourier Transform?  A visual introduction.
 
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An animated introduction to the Fourier Transform, winding graphs around circles. Supported by viewers: https://www.patreon.com/3blue1brown Special thanks to these Patrons: http://3b1b.co/fourier-thanks Follow-on video about the uncertainty principle: https://youtu.be/MBnnXbOM5S4 Puzzler at the end by Jane Street: https://janestreet.com/3b1b Music by Vincent Rubinetti: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown
Views: 1517739 3Blue1Brown
Multivariate Industrial Time Series with Cyber-Attack Simulation, Pavel Filonov, bayesgroup.ru
 
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One area that strongly requires a technique for multivariate time series analysis is cyber-security for industrial processes. Deep packet inspection (DPI) tool monitors network protocols and provides visibility of sensor and command values inside technological signals represented as a multivariate time series. We adopted an approach based on an LSTM neural network to monitor and detect faults in industrial multivariate time series data. To validate the approach we created a Modelica model of part of a real gasoil plant. By introducing hacks into the logic of the Modelica model, we were able to generate both the roots and causes of fault behaviour in the plant. Having a self-consistent data set with labelled faults, we used an LSTM architecture with a forecasting error threshold to obtain precision and recall quality metrics. The dependency of the quality metric on the threshold level is considered. An appropriate mechanism such as "one handle" was introduced for filtering faults that are outside of the plant operator field of interest. https://arxiv.org/abs/1612.06676
Views: 221 Oleg Ivanov
6. Monte Carlo Simulation
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses the Monte Carlo simulation, Roulette License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 229698 MIT OpenCourseWare
Dynamic Social Network Analysis: Model, Algorithm, Theory, & Application CMU Research Speaker Series
 
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Across the sciences, a fundamental setting for representing and interpreting information about entities, the structure and organization of communities, and changes in these over time, is a stochastic network that is topologically rewiring and semantically evolving over time, or over a genealogy. While there is a rich literature in modeling invariant networks, until recently, little has been done toward modeling the dynamic processes underlying rewiring networks, and on recovering such networks when they are not observable. In this talk, I will present some recent developments in analyzing what we refer to as the dynamic tomography of evolving networks. I will first present a new class of statistical models known as dynamic exponential random graph models for evolving social networks, which offers both good statistical property and rich expressivity; then, I will present new sparse-coding algorithms for estimating the topological structures of latent evolving networks underlying nonstationary time-series or tree-series of nodal attributes, along with theoretical results on the asymptotic sparsistency of the proposed methods; finally, I will present a new Bayesian model for estimating and visualizing the trajectories of latent multi-functionality of nodal states in the evolving networks. I will show some promising empirical results on recovering and analyzing the latent evolving social networks in the US Senate and the Enron Corporation at a time resolution only limited by sample frequency. In all cases, our methods reveal interesting dynamic patterns in the networks.
Views: 2466 Microsoft Research
Econometrics // Lecture 1: Introduction
 
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This is an introduction to econometrics tutorial. This video is a basic overview and touches on each of these subjects: 1. What is Econometrics? 2. Goals of Econometrics 3. Types of Economic Data 4. The "Simple Linear Regression" (SLR) 5. Causality This lecture on econometric theory is meant to introduce the student to the concepts of econometrics, as well as provide a basic overview of what the topic of econometrics encompasses. The next video tutorial on simple linear regressions: http://youtu.be/CBa8frhRKMw Follow us on Twitter @ https://twitter.com/KeynesAcademy All video, images, commentary and music is owned by Keynes Academy.
Views: 283457 KeynesAcademy
Adventure Time Line / Timeline  |  Cartoon Network Theory
 
11:03
Here is the best Adventure Time-Line that I can come up with (published before the war for the Candy Kingdom). Please note, this is regards to the series, NOT the expanded universe of comics/games. The Fangirl Merchandise is now available at: https://www.teepublic.com/user/thefangirl Shop on Amazon, see The Fangirl's favorite things, and help support this channel for no extra cost to you! https://www.amazon.com/shop/thefangirl Find me elsewhere: https://www.instagram.com/sayhalogoodbye https://www.twitter.com/the_fanily https://www.facebook.com/sayhalogoodbye https://www.youtube.com/sayhalogoodbye https://www.youtube.com/thefanily
Views: 553441 The Fangirl
NIPS 2015 Workshop (Yu) 15512 Time Series Workshop
 
18:48
Data, in the form of time-dependent sequential observations emerge in many key real-world problems ranging from biological data, to financial markets, to weather forecasting and audio/video processing. However, despite the ubiquity of such data, the vast majority of learning algorithms have been primarily developed for the setting in which sample points are drawn i.i.d. from some possibly unknown fixed distribution. While there exist algorithms designed to handle non-i.i.d. data, these typically assume specific parametric form of data-generating distribution. Such assumptions may undermine the possibly complex nature of modern data which can possess long-range dependency patterns that we now have the computing power to discern. On the other extreme, some online learning algorithms consider a non-stochastic framework without any distributional assumptions. However, such methods may fail to fully address the stochastic aspect of real-world time-series data. lt br gt lt br gt The goal of this workshop is to bring together theoretical and applied researchers interested in the analysis of time series, and the development of new algorithms to process sequential data. This includes algorithms for time series prediction, classification, clustering, anomaly and change point detection, correlation discovery, dimensionality reduction as well as a general theory for learning and comparing stochastic processes. We invite researchers from the related areas of batch and online learning, reinforcement learning, data analysis and statistics, econometrics, and many others to contribute to this workshop.
Views: 211 NIPS
Machine Learning for Survival Analysis: Theory, Algorithms and Applications part 1
 
01:48:52
Authors: Yan Li, University of Michigan Chandan K. Reddy, Department of Computer Science, Virginia Polytechnic Institute and State University Abstract: Due to the advancements in various data acquisition and storage technologies, different disciplines have attained the ability to not only accumulate a wide variety of data but also to monitor observations over longer time periods. In many real-world applications, the primary objective of monitoring these observations is to estimate when a particular event of interest will occur in the future. One of the major difficulties in handling such problem is the presence of censoring, i.e., the event of interests is unobservable in some instance which is either because of time limitation or losing track. Due to censoring, standard statistical and machine learning based predictive models cannot readily be applied to analyze the data. An important subfield of statistics called survival analysis provides different mechanisms to handle such censored data problems. In addition to the presence of censoring, such time-to-event data also encounters several other research challenges such as instance/feature correlations, high-dimensionality, temporal dependencies, and difficulty in acquiring sufficient event data in a reasonable amount of time. To tackle such practical concerns, the data mining and machine learning communities have started to develop more sophisticated and effective algorithms that either complement or compete with the traditional statistical methods in survival analysis. In spite of the importance of this problem and relevance to real-world applications, this research topic is scattered across various disciplines. In this tutorial, we will provide a comprehensive and structured overview of both statistical and machine learning based survival analysis methods along with different applications. We will also discuss the commonly used evaluation metrics and other related topics. The material will be coherently organized and presented to help the audience get a clear picture of both the fundamentals and the state-of-the-art techniques. Link to tutorial: http://dmkd.cs.vt.edu/TUTORIAL/Survival/ More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 513 KDD2017 video
Why everyone should learn some Data Science?
 
35:13
Everyone, irrespective career choices should learn some data science. Data Science skills are very useful every where. As part of data science you learn statistical analysis, forecasting, data visualization & mathematical programming that are very useful no matter which career you are interested in . Computational skills are going to be very important in future in any jobs ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
PRMS Time Series
 
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http://gallery.usgs.gov/videos/940 Instructions for time series data preparation when using the USGS Precipitation Runoff Modeling System (PRMS).
Views: 413 USGS
Time Series Analysis II: Advanced Topics
 
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Mark Pickup, Associate Professor in the Department of Political Science at Simon Fraser University, and Paul Kellstedt, Associate Professor of Political Science at Texas A&M University, describe their ICPSR Summer Program workshop "Time Series Analysis II: Advanced Topics."
NIPS 2015 Workshop (Kom Samo) 15506 Time Series Workshop
 
16:23
Data, in the form of time-dependent sequential observations emerge in many key real-world problems ranging from biological data, to financial markets, to weather forecasting and audio/video processing. However, despite the ubiquity of such data, the vast majority of learning algorithms have been primarily developed for the setting in which sample points are drawn i.i.d. from some possibly unknown fixed distribution. While there exist algorithms designed to handle non-i.i.d. data, these typically assume specific parametric form of data-generating distribution. Such assumptions may undermine the possibly complex nature of modern data which can possess long-range dependency patterns that we now have the computing power to discern. On the other extreme, some online learning algorithms consider a non-stochastic framework without any distributional assumptions. However, such methods may fail to fully address the stochastic aspect of real-world time-series data. lt br gt lt br gt The goal of this workshop is to bring together theoretical and applied researchers interested in the analysis of time series, and the development of new algorithms to process sequential data. This includes algorithms for time series prediction, classification, clustering, anomaly and change point detection, correlation discovery, dimensionality reduction as well as a general theory for learning and comparing stochastic processes. We invite researchers from the related areas of batch and online learning, reinforcement learning, data analysis and statistics, econometrics, and many others to contribute to this workshop.
Views: 151 NIPS
Data Scientist Job Descriptions & Roles
 
11:19
Data Scientist Job description can be of many types. Depending on whether the company is hiring for R&D , for business analysis or for Automation, the job description would vary a great deal. How ever there are a few common skills that can come handy in all kinds of roles 1- Statistics , Maths 2- Machine Learning, Deep Learning 3- Python/R/SQL 4- Bog Data Tools ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Time Complexity Analysis: Data structures -IA Information Assistant Exam 2018 - IBPS RRB IT Officer
 
10:23
Now You Can Buy Very Important E-Study Material for your Exam Click Below Link-(FOR KVS PGT COMPUTER IBPS IT, RRB IT, SBI IT, GATE CSE, UGC CS, PROGRAMMER, & IA) 1, Hands On DATABASE 2000 MCQ Test Series- Buy Link- https://goo.gl/NzD6r3 2, Hands on Operating Systems 1500 VERY IMP.MCQ Series- Buy Link- https://goo.gl/t1DPpR 3, Hands on Computer Networks 1500 VERY IMP.MCQ Series Buy Link- https://goo.gl/LvzNWm 4, Hands on Data Structures & Algorithms 1500+ MCQ Buy Link- https://goo.gl/j82wUK 5, Hands On Object Oriented Programming-1500+ MCQ Series Buy Link- https://goo.gl/KYeC5g 6, Hands On Software Engineering -1000 IMP.MCQ Series Buy Link- https://goo.gl/oBVtv9 7, Hands On Computer Science and IT 1000 IMP.MCQ Series Buy Link- https://goo.gl/GFEjb9 8, Hands On Computer Architecture -1500 IMP.MCQ Series Buy Link- http://goo.gl/joUVPH 9, Hands On RDBMS -1500 IMP.MCQ Series Buy Link- https://goo.gl/DtG31d 10, Hands On Computer Fundamentals -1500 IMP.MCQ Series Coming Soon Team Writing on it.. 11, CRACKING THE CODING INTERVIEW. 2000+ JAVA INTERVIEW QUESTION & ANSWERS AND 200+ SIMPLE INTERVIEW QUESTIONS. https://goo.gl/75ytgQ How to crack RRB IT Officer Exam 100% -IBPS IT- SBI IT- IBPS RRB IT OFFICER (Download 15000+ MCQ) kvs pgt,kendriya vidyalaya sangathan,kvs pgt exam,kvs pgt syllabus,kvs pgt selection process,kvs pgt recruitment,kvs best books for pgt,pgt syllabus,kvs,kvs recruitment 2018,best book for kvs pgt,mentors 36,kvs eligibility pgt #kvs pgt,#kendriya vidyalaya sangathan,#kvs pgt exam,#kvs pgt syllabus,#kvs pgt selection process,#kvs pgt recruitment,#kvs best books for pgt,#pgt syllabus,#kvs,#kvs recruitment 2018,#best book for kvs pgt,#mentors 36,#kvs eligibility pgt
Views: 6009 IT EXAM GURU JI
Building Random Forests Models with Treenet and LAGS for Time Series Modeling
 
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http://www.salford-systems.com/home/downloadspm How to build a Random Forests model using TreeNet Stochastic Gradient Boosting technology. Additionally, this tutorial shows how to do time-series style modeling in the Salford Predictive Modeler software suite by using the LAGS function.
Views: 815 Salford Systems
Measuring Personality: Crash Course Psychology #22
 
11:08
You can directly support Crash Course at http://www.subbable.com/crashcourse Subscribe for as little as $0 to keep up with everything we're doing. Also, if you can afford to pay a little every month, it really helps us to continue producing great content. How would you measure a personality? What, exactly, is the self? Well, as you've come to expect, it's not that easy to nail down an answer for those questions. Whether you're into blood, bile, earth, wind, fire, or those Buzzfeed questionnaires, there are LOTS of ways to get at who we are and why. -- Table of Contents Trait & Social-Cognitive Personality 01:35:01 Measuring Personality 02:57:03 Who or What is the Self? 09:16:14 How Self Esteem Works 09:42:04 -- Want to find Crash Course elsewhere on the internet? Facebook - http://www.facebook.com/YouTubeCrashCourse Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support CrashCourse on Subbable: http://subbable.com/crashcourse
Views: 1595106 CrashCourse
Happening Happened (Song Clip) | Adventure Time (Series Finale) - Come Along With Me
 
02:43
this song is called "Time Adventure" originally by Rebecca Sugar, the creator of Steven Universe original video: https://youtu.be/Xr53S9vIbCE "Come Along With Me" is the last episode of Season 9 and the series.. I do NOT own anything, all rights goes to Pendleton Ward, Cartoon Network
Views: 157556 KittenWispy
Strurel Tutorial: Part 1 – Theory of Reliability Analysis
 
15:59
This tutorial series explains and demonstrates how to use the Strurel programs. To learn more about Strurel, please visit http://strurel.com/
Views: 1925 STRUREL
NIPS 2015 Workshop (Dodge) 15507 Time Series Workshop
 
28:21
Data, in the form of time-dependent sequential observations emerge in many key real-world problems ranging from biological data, to financial markets, to weather forecasting and audio/video processing. However, despite the ubiquity of such data, the vast majority of learning algorithms have been primarily developed for the setting in which sample points are drawn i.i.d. from some possibly unknown fixed distribution. While there exist algorithms designed to handle non-i.i.d. data, these typically assume specific parametric form of data-generating distribution. Such assumptions may undermine the possibly complex nature of modern data which can possess long-range dependency patterns that we now have the computing power to discern. On the other extreme, some online learning algorithms consider a non-stochastic framework without any distributional assumptions. However, such methods may fail to fully address the stochastic aspect of real-world time-series data. lt br gt lt br gt The goal of this workshop is to bring together theoretical and applied researchers interested in the analysis of time series, and the development of new algorithms to process sequential data. This includes algorithms for time series prediction, classification, clustering, anomaly and change point detection, correlation discovery, dimensionality reduction as well as a general theory for learning and comparing stochastic processes. We invite researchers from the related areas of batch and online learning, reinforcement learning, data analysis and statistics, econometrics, and many others to contribute to this workshop.
Views: 139 NIPS
What is econometrics?
 
07:46
This video provides an introduction to the subject of econometrics, using a few examples to explain the sorts of question which are likely to be encountered. In this video I want to talk about what actually do we mean by econometrics. So econometrics is in general a statistical tool set which helps us to evaluate some sort of relationship of interest. An example might be we're interested in, for individuals, what is the effect of an individual's level of education on the average wage which that individual might expect to obtain. So if an individual's level of education increases we might expect that the level of wages which an individual obtains on average might increase. So if I was to plot a graph of the level of education of individuals on the x-axis, against the wages which a group of individuals have obtained on the y-axis, then we might hope to see some sort of positive correlation between these two variables. That's not to say that is necessarily a causal relationship only that there is some sort of positive relationship between these two variables. Econometrics help us to quantify this degree of correlation by in a sense drawing a line through the centre of all those points. And by drawing a line through the centre of all those points we are hoping to capture what is the average effect of education on wages. So on average an individual who has years of education, might expect to obtain a wage which is let's say four hundred dollars. Whereas an individual perhaps if they had a year's worth of education might expect back their wages to go up by a hundred dollars. So they now earn five hundred dollars. Well econometrics is a toolset for finding out what the strength of this relationship is. So how much do wages actually go up by. And this type of relationship here where we're concerned with the relationship for individual people or individual firms is the subject of microeconometrics. And it's called microeconometrics for analogy with microeconomics. Another sort of microeconometric relationship we might be interested in might be, 'What is the effect of TV advertising on a company's level of sales. So if I was to draw a graph of a company's level of sales over time then we might have something which looks something like this. If there is some sort of seasonality. Perhaps this is coffee sales or ice cream sales. And we might be interested in do these peaks which we observe in the data - are they caused by TV advertising? And TV advertising might look something like these bars that I have drawn below. So econometrics is a way of understanding for this time series data does this TV advertising here cause sales to go up? And similarly does this TV advertising here cause sales to go up. So this is slightly different to the previous example in that we are dealing with what we call time series data, whereas the original data was what we call cross-sectional data. But it's still actually what we call Microeconometric data because we are dealing with data for a particular firm. Another type of econometrics is the subject of macroeconometrics and macroeconometrics as its name suggests is to deal with macro relationships. So an example here might be what is the effect of interest rate falls on inflation. So traditional economic theory suggests that if the interest rate falls then the inflation rate should increase because of increases in aggregate demand. So econometrics is a way of quantifying that particular relationship. So all of these relationships are variations on the same sort of theme which we see time and time again in econometrics. So the idea with econometrics is that there is some sort of population or in the case of time series data we actually call this a data generating process but you can kind of think about it for now at least in terms of a population. And within this population there is some sort of true relationship between the variables which are interested in. So there might be some sort of true relationship between an individual's level of wages and let's say their level of education. So in this example here beta quantifies the effects of one year of education on an individual's level of average wages. But there are other factors which also determine wages, which we group together in our population error 'u', which are all these other idiosyncratic factors which affect an individual's level of wages. So an example might be 'where does the individual live?' Are they based in OECD countries for example? What are their interests? Are they interested in pursuing a career where they earn lots of money like an investment banker or are they interested in going into the civil service for example? Those are the sorts of things which are contained within the population error 'u'... Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses.
Views: 139583 Ben Lambert
Visual Analytics for Model Selection in Time Series Analysis - TiMoVA
 
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TiMoVA provides visual guidance for domain experts in the task of time series model selection. For more details visit: http://www.cvast.tuwien.ac.at/TiMoVA
Views: 465 iegisistuw