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Time series inference with nonlinear dynamics and filtering for control.
 
20:32
Many tasks in finance, science and engineering require the ability to control a dynamic system to maximise some objective. Designing controllers based on a physical understanding of systems is often time consuming and inaccurate. One is generally forced to make an increasing amount of simplifying assumptions the more complex the system is. For instance, friction, `stiction' and flex are difficult to model and, thus, frequently ignored. An alternative is automatic learning of control. By training a probabilistic model of a system's dynamics directly from data, we can predict how the system will evolve over time. Such predictive power enables evaluation and comparison of controllers by simulating how they would effect a system. Controller design can thus be seen as the process of finding which controller would optimise a user-supplied objective function in simulation.
Views: 461 Microsoft Research
Nonlinear time series analysis
 
01:04:52
Views: 1893 SCMKeele
8. Time Series Analysis I
 
01:16:19
MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Peter Kempthorne This is the first of three lectures introducing the topic of time series analysis, describing stochastic processes by applying regression and stationarity models. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 183529 MIT OpenCourseWare
Time Series - 6 Non-linear Trend - Second Degree Parabola - Quadratic Method
 
16:30
#Statistics #Time #Series #Business #Forecasting #NonLinear #Trend #Values #SecondDegree #Parabola #Quadratic Definitions “A time series may be defined as a sequence of values of same variable corresponding to successive points in time.” – W. Z. Hersch “A time series may be defined as a sequence of repeated measurement of a variable made periodically through time.” – Cecil H. Mayers Analysis of Time Series “The main object of analyzing time series is to understand, interpret and evaluate changes in economic phenomena in the hope of more correctly anticipating the course of future events.” – Hersch A time series is a dynamic distribution, which reveals a good deal of variations over time. Statistical methods are, therefore, required to analyze various types of movements in a time series. There may be cyclical variations in general business activity and there may be short duration seasonal variations. There are also some accidental and random variables. The primary purpose of the analysis of time series is to discover and measure all such types of variations, which characterize a time series. Time series analysis means analyzing the historical patterns of the variable that have occurred in past as a means of predicting the future value of the variable. It helps to identify and explain the following: (i) Any regular or systematic variation in the series of data which is due to seasonality- the ‘seasonal’ (ii) Cyclical patterns. (iii) Trends in the data. (iv) Growth rates of these trends. This method can be useful when no major environmental changes are expected and it does highlight seasonal variations in sales and consumer demand. However, time series analysis is limited when organizations face volatile environments. Components of Time series – The time series are classified into four basic types of variations which are analyzed below: T = Trend S = Seasonal variations C = Cyclic variations I = Irregular fluctuations. This composite series is symbolized by the following general terms: O = T x S x C x I Where O = Original data T = Trend S = Seasonal variations C = Cyclic variations I = Irregular components. This Multiplicative model is to be used when S, C, and I are given in percentages. If, however, their true (absolute) values are known the model takes the additive form i.e., O=T+C+S+I. Algebraic Method For Finding Trend (Method of curve fitting by the principle of Least Squares) Fitting of Non-linear Trend Second Degree parabola / Quadratic Method Standard Equation y = a + bx + cx^2 The following three normal equations are used for estimating 'a', 'b' and 'C'. Normal Equations (i) Σy = na + bΣx + cΣx^2 (ii) Σxy = nΣx + bΣx2 + cΣx^3 (iii) Σx^2y = aΣx^2 + bΣx^3 + cΣx^4 Case Fit a parabola y = a + bx + cx^2 to this data. Estimate the price of the commodity for the year 2017 Year 2011 2012 2013 2014 2015 2016 Price 100 107 128 140 181 299 Time Series, Non-linear Trend, Second Degree Parabola, Method of Least Squares, Statistics, MBA, MCA, BE, CA, CS, CWA, CMA, CPA, CFA, BBA, BCom, MCom, BTech, MTech, CAIIB, FIII, Graduation, Post Graduation, BSc, MSc, BA, MA, Diploma, Production, Finance, Management, Commerce, Engineering , Grade-11, Grade- 12 - www.prashantpuaar.com
Views: 13931 Prashant Puaar
Time Series - 7 Non-linear Trend - Exponential Trend
 
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#Statistics #Time #Series #Business #Forecasting #NonLinear #Trend #Values #Exponential #Curve #Fitting Exponential Curve Fitting / Exponential Trend Method: Standard Equation: y = AB^x Log y = Log A + x Log B Normal Equations: (i) ∑Log y = n [Log A] + ∑x[Log B] (ii) ∑x Log y = ∑x [Log A] + ∑x^2 [Log B] If we take Y = Log y a = Log A b = Log B, the normal equations will be like - \ ΣY = na + bΣx ΣxY = aΣx + bΣx^2 Case Fit Exponential Curve from the following data. Also find trend values and estimate for 2017: Year 2012 2013 2014 2015 2016 Price 1.6 4.5 13.8 40.2 125 Time Series, Non-linear Trend, Exponential Trend, Method of Least Squares, Statistics, MBA, MCA, BE, CA, CS, CWA, CMA, CPA, CFA, BBA, BCom, MCom, BTech, MTech, CAIIB, FIII, Graduation,Post Graduation, BSc, MSc, BA, MA, Diploma, Production, Finance, Management, Commerce, Engineering , Grade-11, Grade- 12 - www.prashantpuaar.com
Views: 7379 Prashant Puaar
Autoregressive vs. Moving Average: Difference between AR and MA in Microsoft Excel
 
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1. Example Dataset (FBExample.csv) Download Here: https://drive.google.com/open?id=1zLdsfBk8T31pEnm61trfb9hFMjmewGb5 2. MA Analysis in Python https://www.youtube.com/watch?v=TOeXpCHtrxk
Views: 29959 The Data Science Show
Time Series Talk : Moving Average Model
 
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A gentle intro to the Moving Average model in Time Series Analysis
Views: 155 ritvikmath
Time Series In R | Time Series Forecasting | Time Series Analysis | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) In this Edureka YouTube live session, we will show you how to use the Time Series Analysis in R to predict the future! Below are the topics we will cover in this live session: 1. Why Time Series Analysis? 2. What is Time Series Analysis? 3. When Not to use Time Series Analysis? 4. Components of Time Series Algorithm 5. Demo on Time Series For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 87792 edureka!
Autoregressive vs Moving Average Order One processes - part 1
 
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This video provides a methodology for diagnosing whether a given series is AR(1) or MA(1). Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti
Views: 59302 Ben Lambert
Forecasting time series using R by Prof Rob J Hyndman at Melbourne R Users
 
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Presenter: Prof Rob J Hyndman Slides available: http://robjhyndman.com/talks/melbournerug/ Melbourne R Users: http://www.meetup.com/MelbURN-Melbourne-Users-of-R-Network/ Other R User Group Videos: http://www.vcasmo.com/user/drewconway Info on upcoming book: http://robjhyndman.com/researchtips/fpp/ I will look at the various facilities for time series forecasting available in R, concentrating on the forecast package. This package implements several automatic methods for forecasting time series including foreasts from ARIMA models, ARFIMA models and exponential smoothing models. I will also look more generally at how to go about forecasting non-seasonal data, seasonal data, seasonal data with high frequency, and seasonal data with multiple frequencies. Examples will be taken from my own consulting experience. I will give an overview of what's possible and available and where it is useful, rather than give the mathematical details of any specific time series methods. Rob J Hyndman is Professor of Statistics at Monash University and Director of the Monash University Business and Economic Forecasting Unit. He completed a science degree at the University of Melbourne in 1988 and a PhD on nonlinear time series modelling at the same university in 1992. He has worked at the University of Melbourne, Colorado State University, the Australian National University and Monash University. Rob is Editor-in-Chief of the "International Journal of Forecasting" and a Director of the International Institute of Forecasters. He has written over 100 research papers in statistical science. In 2007, he received the Moran medal from the Australian Academy of Science for his contributions to statistical research. Rob is co-author of the well-known textbook "Forecasting: methods and applications" (Wiley, 3rd ed., 1998) and of the book "Forecasting with exponential smoothing: the state space approach" (Springer, 2008). He is also the author of the widely-used "forecast" package for R. For over 25 years, Rob has maintained an active consulting practice, assisting hundreds of companies and organizations on forecasting problems. His recent consulting work has involved forecasting electricity demand, tourism demand and the Australian government health budget. More information is available on his website at robjhyndman.com. Thank you to Pedro Olaya for filming the talk and Deloitte for providing the venue.
Views: 71881 Jeromy Anglim
Non-linear time:  A layperson's perspective (expanded)
 
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3 existing time theories, a personal experience, and a proposal for new theories.
Views: 8212 Kris Fuehr
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: 67442 Hvass Laboratories
NONLINEAR DYNAMIC TIME HISTORY ANALYSIS IN ETABS
 
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THIS IMPORTANT TUTORIAL HAS BEEN PREPARED BASED ON REQUEST OF SOME SUBSCRIBERS.
Views: 22569 DECODE BD
Time Series - 5 Method Least Squares - Non-linear Trend - Second Degree Parabola
 
15:37
#Statistics #Time #Series #Business #Forecasting #NonLinear #Trend #Values #LeastSquares #SecondDegree #Parabola Definitions “A time series may be defined as a sequence of values of same variable corresponding to successive points in time.” – W. Z. Hersch “A time series may be defined as a sequence of repeated measurement of a variable made periodically through time.” – Cecil H. Mayers Analysis of Time Series “The main object of analyzing time series is to understand, interpret and evaluate changes in economic phenomena in the hope of more correctly anticipating the course of future events.” – Hersch A time series is a dynamic distribution, which reveals a good deal of variations over time. Statistical methods are, therefore, required to analyze various types of movements in a time series. There may be cyclical variations in general business activity and there may be short duration seasonal variations. There are also some accidental and random variables. The primary purpose of the analysis of time series is to discover and measure all such types of variations, which characterize a time series. Time series analysis means analyzing the historical patterns of the variable that have occurred in past as a means of predicting the future value of the variable. It helps to identify and explain the following: (i) Any regular or systematic variation in the series of data which is due to seasonality- the ‘seasonal’ (ii) Cyclical patterns. (iii) Trends in the data. (iv) Growth rates of these trends. This method can be useful when no major environmental changes are expected and it does highlight seasonal variations in sales and consumer demand. However, time series analysis is limited when organizations face volatile environments. Components of Time series – The time series are classified into four basic types of variations which are analyzed below: T = Trend S = Seasonal variations C = Cyclic variations I = Irregular fluctuations. This composite series is symbolized by the following general terms: O = T x S x C x I Where O = Original data T = Trend S = Seasonal variations C = Cyclic variations I = Irregular components. This Multiplicative model is to be used when S, C, and I are given in percentages. If, however, their true (absolute) values are known the model takes the additive form i.e., O=T+C+S+I. Algebraic Method For Finding Trend (Method of curve fitting by the principle of Least Squares) Fitting of Non-linear Trend Second Degree parabola / Quadratic Method Standard Equation y = a + bx + cx^2 The following three normal equations are used for estimating 'a', 'b' and 'C'. Normal Equations (i) Σy = na + bΣx + cΣx^2 (ii) Σxy = nΣx + bΣx2 + cΣx^3 (iii) Σx^2y = aΣx^2 + bΣx^3 + cΣx^4 Case Fit a parabolic curve of second degree to the following data: Year 2010 2011 2012 2013 2014 2015 2016 Value 10 12 18 15 13 16 14 Time Series, Non-linear Trend, Second Degree Parabola, Method of Least Squares, Statistics, MBA, MCA, BE, CA, CS, CWA, CMA, CPA, CFA, BBA, BCom, MCom, BTech, MTech, CAIIB, FIII, Graduation, Post Graduation, BSc, MSc, BA, MA, Diploma, Production, Finance, Management, Commerce, Engineering , Grade-11, Grade- 12 - www.prashantpuaar.com
Views: 13617 Prashant Puaar
NonLinear Time Series Analysis in C#.NET
 
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NonLinear Time Series Analysis in C#.NET using both Math.Net and Cronos for both Linear and Nonlinear(i.e. Mackey-Glass equations) Granger Causality for simulated data, Threshold Autoregressive Models(TAR) and Functional Autogressive Models(FAR) are shown. This is a just a small sample of what can be done. More fully developed models are part of the BMI-1 code base for the specification, estimation and forecasting of nonparametric and parametric nonlinear time series models that can be used as web services with Time Maestro, Model Maker 2 and other applications.
Views: 3414 TheCromwellWorkshop
Time Series Analysis (Georgia Tech) - 5.2.4 - Functional Data Analysis
 
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Time Series Analysis PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech Unit 5: Other Time Series Methods Part 2: Multivariate Time Series Modelling Lesson: 4 - Functional Data Analysis Notes, Code, Data: https://tinyurl.com/Time-Series-Analysis-NotesData
Views: 261 Bob Trenwith
Statistics VIII - Time Series Forecasting
 
12:42
Learn to do some basic Time Series Forecasting using Excel.
Views: 3110 ExcelStatistics
Nonlinear Dynamics: Time Series Analysis and the Observer Problem
 
09:33
These are videos from the Nonlinear Dynamics course offered on Complexity Explorer (complexity explorer.org) taught by Prof. Liz Bradley. These videos provide a broad introduction to the field of nonlinear dynamics, focusing both on the mathematics and the computational tools that are so important in the study of chaotic systems. The course is aimed at students who have had at least one semester of college-level calculus and physics, and who can program in at least one high-level language (C, Java, Matlab, R, ...). After a quick overview of the field and its history, we review the basic background that students need in order to succeed in this course. We then dig deeper into the dynamics of maps—discrete-time dynamical systems—encountering and unpacking the notions of state space, trajectories, attractors and basins of attraction, stability and instability, bifurcations, and the Feigenbaum number. We then move to the study of flows, where we revisit many of the same notions in the context of continuous-time dynamical systems. Since chaotic systems cannot, by definition, be solved in closed form, we spend some time thinking about how to solve them numerically, and learning what challenges arise in that process. We then learn about techniques and tools for applying all of this theory to real-world data and close with a number of interesting applications: control of chaos, prediction of chaotic systems, chaos in the solar system, and uses of chaos in music and dance. In each unit of this course, students will begin with paper-and-pencil exercises regarding the corresponding topics, and then write computer programs that operationalize the associated mathematical algorithms. This will not require expert programming skill, but you should be comfortable translating basic mathematical ideas into code. Any computer language that supports simple plotting—points on labelled axes—will suffice for these exercises. We will not ask you to turn in your code, but simply report and analyze the results that your code produces.
Views: 1056 Complexity Explorer
Time Series - 8 Non-linear Trend - Exponential Curve Fitting
 
12:51
#Statistics #Time #Series #Business #Forecasting #NonLinear #Trend #Values #Exponential #Curve #Fitting Exponential Curve Fitting / Exponential Trend Method: Standard Equation: y = AB^x Log y = Log A + x Log B Normal Equations: (i) ∑Log y = n [Log A] + ∑x[Log B] (ii) ∑x Log y = ∑x [Log A] + ∑x^2 [Log B] If we take Y = Log y a = Log A b = Log B, the normal equations will be like - \ ΣY = na + bΣx ΣxY = aΣx + bΣx^2 Case Fit Exponential Curve from the following data. Also find estimate for 20: Year 1946 1956 1966 1976 1986 1996 2006 2016 Value 3.9 5.3 7.3 9.6 12.9 17.1 23.2 30.9 Time Series, Non-linear Trend, Exponential Trend, Method of Least Squares, Statistics, MBA, MCA, BE, CA, CS, CWA, CMA, CPA, CFA, BBA, BCom, MCom, BTech, MTech, CAIIB, FIII, Graduation,Post Graduation, BSc, MSc, BA, MA, Diploma, Production, Finance, Management, Commerce, Engineering , Grade-11, Grade- 12 - www.prashantpuaar.com
Views: 3544 Prashant Puaar
Lecture - 28 Analysis of Chaotic Time Series
 
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Lecture Series on Chaos, Fractals and Dynamical Systems by Prof.S.Banerjee,Department of Electrical Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in.
Views: 11878 nptelhrd
MATLAB Applications - (NAR) Time Series Neural Networks
 
05:53
Taking a look at seasonal data (Sunspots) and creating a function that can be used to predict values in the future. (Recorded with http://screencast-o-matic.com)
Views: 4066 Nick Losee
Time Series Analysis with Python Intermediate | SciPy 2016 Tutorial | Aileen Nielsen
 
03:03:25
Tutorial materials for the Time Series Analysis tutorial including notebooks may be found here: https://github.com/AileenNielsen/TimeSeriesAnalysisWithPython See the complete SciPy 2016 Conference talk & tutorial playlist here: https://www.youtube.com/playlist?list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6.
Views: 64279 Enthought
Lecture - 29 Analysis of Chaotic Time Series
 
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Lecture Series on Chaos, Fractals and Dynamical Systems by Prof.S.Banerjee,Department of Electrical Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in.
Views: 7819 nptelhrd
Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka
 
38:20
** Python Data Science Training : https://www.edureka.co/python ** This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial: 1. Why Time Series? 2. What is Time Series? 3. Components of Time Series 4. When not to use Time Series 5. What is Stationarity? 6. ARIMA Model 7. Demo: Forecast Future Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #timeseries #timeseriespython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 83192 edureka!
Time Series Analysis via Matrix Estimation
 
30:05
Devavrat Shah, Massachusetts Institute of Technology https://simons.berkeley.edu/talks/devavrat-shah-3-29-18 Societal Networks
Views: 1012 Simons Institute
Time Series Analysis - 1 | Time Series in Excel | Time Series Forecasting | Data Science|Simplilearn
 
32:49
This Time Series Analysis (Part-1) tutorial will help you understand what is time series, why time series, components of time series, when not to use time series, why does a time series have to be stationary, how to make a time series stationary and at the end, you will also see a use case where we will forecast car sales for 5th year using the given data. Link to Time Series Analysis Part-2: https://www.youtube.com/watch?v=Y5T3ZEMZZKs You can also go through the slides here: https://goo.gl/RsAEB8 A time series is a sequence of data being recorded at specific time intervals. The past values are analyzed to forecast a future which is time-dependent. Compared to other forecast algorithms, with time series we deal with a single variable which is dependent on time. So, lets deep dive into this video and understand what is time series and how to implement time series using R. Below topics are explained in this " Time Series in R Tutorial " - 1. Why time series? 2. What is time series? 3. Components of a time series 4. When not to use time series? 5. Why does a time series have to be stationary? 6. How to make a time series stationary? 7. Example: Forecast car sales for the 5th year To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment. Why learn Data Science with R? 1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc 2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019 3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709 4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice. 1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. 2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing. 3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice. The Data Science with R is recommended for: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Time-Series-Analysis-gj4L2isnOf8&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn/ - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 32631 Simplilearn
Time Series Analysis (Georgia Tech) - 3.1.2 - Multivariate Time Series - Basic Concepts
 
06:28
Time Series Analysis PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech Unit 3: Multivariate Time Series Modelling Part 1: Multivariate Time Series Lesson: 2 - Multivariate Time Series - Basic Concepts Notes, Code, Data: https://tinyurl.com/Time-Series-Analysis-NotesData
Views: 295 Bob Trenwith
Working with Time Series Data in MATLAB
 
53:29
See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r A key challenge with the growing volume of measured data in the energy sector is the preparation of the data for analysis. This challenge comes from data being stored in multiple locations, in multiple formats, and with multiple sampling rates. This presentation considers the collection of time-series data sets from multiple sources including Excel files, SQL databases, and data historians. Techniques for preprocessing the data sets are shown, including synchronizing the data sets to a common time reference, assessing data quality, and dealing with bad data. We then show how subsets of the data can be extracted to simplify further analysis. About the Presenter: Abhaya is an Application Engineer at MathWorks Australia where he applies methods from the fields of mathematical and physical modelling, optimisation, signal processing, statistics and data analysis across a range of industries. Abhaya holds a Ph.D. and a B.E. (Software Engineering) both from the University of Sydney, Australia. In his research he focused on array signal processing for audio and acoustics and he designed, developed and built a dual concentric spherical microphone array for broadband sound field recording and beam forming.
Views: 56938 MATLAB
Nonlinear Dynamics: Time Series Analysis and the Observer Problem Quiz Solutions
 
02:34
These are videos from the Nonlinear Dynamics course offered on Complexity Explorer (complexity explorer.org) taught by Prof. Liz Bradley. These videos provide a broad introduction to the field of nonlinear dynamics, focusing both on the mathematics and the computational tools that are so important in the study of chaotic systems. The course is aimed at students who have had at least one semester of college-level calculus and physics, and who can program in at least one high-level language (C, Java, Matlab, R, ...). After a quick overview of the field and its history, we review the basic background that students need in order to succeed in this course. We then dig deeper into the dynamics of maps—discrete-time dynamical systems—encountering and unpacking the notions of state space, trajectories, attractors and basins of attraction, stability and instability, bifurcations, and the Feigenbaum number. We then move to the study of flows, where we revisit many of the same notions in the context of continuous-time dynamical systems. Since chaotic systems cannot, by definition, be solved in closed form, we spend some time thinking about how to solve them numerically, and learning what challenges arise in that process. We then learn about techniques and tools for applying all of this theory to real-world data and close with a number of interesting applications: control of chaos, prediction of chaotic systems, chaos in the solar system, and uses of chaos in music and dance. In each unit of this course, students will begin with paper-and-pencil exercises regarding the corresponding topics, and then write computer programs that operationalize the associated mathematical algorithms. This will not require expert programming skill, but you should be comfortable translating basic mathematical ideas into code. Any computer language that supports simple plotting—points on labelled axes—will suffice for these exercises. We will not ask you to turn in your code, but simply report and analyze the results that your code produces.
Views: 366 Complexity Explorer
Neural Networks for Time Series Prediction
 
01:02:20
Scott Crespo will cover the fundamentals of time series prediction and neural networks, and how to implement these sequence predictors using Python, TensorFlow, and Keras. Time series predictors that use neural networks have found applications in areas such as high-frequency trading, smart grid technology, and business intelligence. Due to the unique nature of neural networks, they can efficiently learn from datasets that may have an extremely large number of features and observations.
Python Tutorial - Time Series Analysis with Pandas
 
07:25
Simple technical analysis on Jakarta Composite Index (JCI) [Indeks Harga Saham gabungan] 1997 - 2013 using python, pandas, numpy and matplotlib modules. Installation: sudo apt-get install python-pandas sudo apt-get install python-numpy sudo apt-get install python matplotlib
Views: 20266 kholidfu
Time Series - 4 - Trend Estimation
 
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The fourth in a five-part series on time series data. In this video, I explain how to use an additive decomposition model to: - use regression methods to estimate trend - use dummy variables to estimate seasonal influences - forecast with and without seasonal influences
Views: 14115 Jason Delaney
Nonlinear Regression Using Excel
 
23:28
Nonlinear Regression Using Excel
Views: 46328 Sam Hijazi
Time Series Analysis - 3.4.1 - Difference equations
 
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Practical Time Series Analysis PLAYLIST: https://tinyurl.com/TimeSeriesPlaylist 3 - Stationarity 4.1 - Difference equations
Views: 495 Bob Trenwith
Equation and parameter free dynamical modeling of natural time series
 
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This video gives a cursory overview of the tools for natural time series analysis developed by the Sugihara lab at Scripps Institution of Oceanography (UCSD). The methods discussed give an introduction to Takens theorem and follow the principles of avoiding equations and free parameters so as to avoid overfitting. All approaches use principles from nonlinear dynamics and chaos theory and are numerical rather than equation based. For more information on the subject follow the links below. http://deepeco.ucsd.edu/ https://www.quantamagazine.org/20151013-chaos-theory-and-ecology/ http://www.pnas.org/content/112/13/3856.full http://www.ncbi.nlm.nih.gov/pubmed/22997134
#1 - Dr. Chris Kulp - Nonlinear Dynamics and Chaos, Machine Learning, and Artificial Intelligence
 
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Dr. Chris Kulp is a Professor of Physics at Lycoming College in Williamsport, PA and an avid researcher in the field of nonlinear dynamics and chaos. He specializes in Nonlinear Time Series Analysis. In this episode, we discuss nonlinear and chaotic systems and how he studies them. We also discuss the growing field of machine learning and its applications in industry and academia. Artificial Intelligence and its implications on the work force are also discussed. #NonlinearScience #Physics #Chaotic For more episodes or information about "The State of The Universe with Brendan Drachler" visit thestateoftheuniverse.com or follow Brendan on Twitter and Instagram @BrendanDrachler. The State of the Universe is an accessible science and social podcast hosted by Astrophysicist Brendan Drachler. Listen to Brendan and other renowned members of the scientific community discuss and explain the cutting edge research occurring across the world today!
Forecasting - Linear regression - Example 1 - Part 1
 
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In this video, you will learn how to find the demand forecast using linear regression.
Views: 75278 maxus knowledge
Developing Forecast Models from Time Series Data in MATLAB Part 2
 
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Developing Forecast Models from Time Series Data in MATLAB Part 2 Abhaya Parthy, MathWorks Are you looking to increase your data analysis capabilities? Do you need to perform complex analytics and automate cumbersome repetitive tasks such as batch processing? Do you need to make your programs accessible to others? During this presentation, we demonstrate how you can use MATLAB to develop nonlinear predictive models from historical time-series measurements. As a working case study, a forecast model of short-term electricity loads for the Australian market using BOM and AEMO data is presented. This case study applies nonlinear tree bagging regression and neural network modelling techniques. At the end of the case study, the MATLAB forecast model is converted into a deployable plug-in for Microsoft Excel. #Time_Series_Data_in_MATLAB
Views: 114 TO Courses
James Hughes: Finding Nonlinear Relationships in fMRI Time Series
 
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The brain is an intrinsically nonlinear system, yet the dominant methods used to generate network models of functional connectivity from fMRI data use linear methods. Although these approaches have been used successfully, they are limited in that they can find only linear relations within a system we know to be nonlinear. This study employs a highly specialized genetic programming system which incorporates multiple enhancements to perform symbolic regression, a sophisticated and computationally rigorous type of regression analysis that searches for declarative mathematical expressions to describe relationships in observed data with any provided set of basis functions. Publicly available fMRI data from the Human Connectome Project were segmented into meaningful regions of interest and highly nonlinear mathematical expressions describing functional connectivity were generated. These nonlinear expressions exceed the explanatory power of traditional linear models and allow for more accurate investigation of the underlying physiological connectivities. Computationally Assisted Mathematical Discovery and Experimental Mathematics: ACMES 2 12-15 May 2016, London, Ontario, Canada James Hughes, Department of Computer Science, Western University May 14, 2016 Visit the Rotman website for more information on applications, events, project descriptions and openings. http://www.rotman.uwo.ca Follow The Rotman Institute on Twitter: https://twitter.com/rotmanphilo Like The Rotman Institute on Facebook: https://www.facebook.com/rotmanphilosophy Subscribe to our channel: https://www.youtube.com/user/rotmanphilosophy
Policy Analysis Using Interrupted Time Series | UBCx on edX | Course About Video
 
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Take this course for on edX: https://www.edx.org/course/policy-analysis-using-interrupted-time-ubcx-itsx ↓ More info below. ↓ Follow on Facebook: www.facebook.com/edx Follow on Twitter: www.twitter.com/edxonline Follow on YouTube: www.youtube.com/user/edxonline ABOUT THIS COURSE Interrupted time series analysis and regression discontinuity designs are two of the most rigorous ways to evaluate policies with routinely collected data. ITSx comprehensively introduces analysts to interrupted time series analysis (ITS) and regression discontinuity designs (RD) from start to finish, including definition of an appropriate research question, selection and setup of data sources, statistical analysis, interpretation and presentation, and identification of potential pitfalls. At the conclusion of the course, students will have all the tools necessary to propose, conduct and correctly interpret an analysis using ITS and RD approaches. This will help them position themselves as a go-to person within their company, government department, or academic department as the technical expert on this topic. ITS and RD designs avoid many of the pitfalls associated with other techniques. As a result of their analytic strength, the use of ITS and RD approaches has been rapidly increasing over the past decade. These studies have cut across the social sciences, including: Studying the effect of traffic speed zones on mortality Quantifying the impact of incentive payments to workers on productivity Assessing whether alcohol policies reduce suicide Measuring the impact of incentive payments to physicians on quality of care Determining whether the use of HPV vaccination influences adolescent sexual behavior WHAT YOU'LL LEARN The strengths and drawbacks of ITS and RD studies Data requirements, setup, and statistical modelling Interpretation of results for non-technical audiences Production of compelling figures
Views: 3827 edX
Time Series - 3 - Smoothing Methods
 
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The third in a five-part series on time series data. In this video, I explain how to use smoothing methods to smooth data series or make forecasts. The methods covered include: - moving averages - centered moving average - weighted moving average - exponential smoothing
Views: 20151 Jason Delaney
ARMA Model | Auto Regressive Moving Average | Time Series
 
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In this video you will learn the theory behind the ARMA process in time series analysis For Analytics Study Packs visit : http://analyticuniversity.com/
Views: 18739 Analytics University
Time Series - 2 - Forecast Error
 
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The second in a five-part series on time series data. In this video, I explain how to evaluate forecasting methods using various measures of forecasting error. The measures covered include: - mean absolute error (MAE) - mean square error (MSE) - mean absolute percentage error (MAPE)
Views: 22366 Jason Delaney
Moving Average Models | Time Series
 
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In this video you will learn the theory behind moving average models For Training & Study packs on Analytics/Data Science/Big Data, Contact us at [email protected] Find all free videos & study packs available with us here: http://analyticuniversity.com/ SUBSCRIBE TO THIS CHANNEL for free tutorials on Analytics/Data Science/Big Data/SAS/R/Hadoop
Views: 19938 Analytics University