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What are Multivariate Time Series Models
 
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Multivariate time series models are different from that of Univariate Time Series models in a way that it also takes structural forms that is it includes lags of different time series variable apart from the lags of it's own. For Study Packs Visit : http://analyticuniversity.com/
Views: 19639 Analytics University
4 2 Simulating Multivariate Time Series in R
 
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http://quantedu.com/wp-content/uploads/2014/04/Time%20Series/4_2%20Simulate_Multivariate
Views: 9506 Quant Education
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
Views: 59196 edureka!
ARIMA and R: Stock Price Forecasting
 
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This tutorial illustrates how to use an ARIMA model to forecast the future values of a stock price. View tutorial at: http://www.michaeljgrogan.com/arima-model-statsmodels-python/
Views: 11800 Michael Grogan
Time Series ARIMA Models in R
 
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Time Series ARIMA Models in R https://sites.google.com/site/econometricsacademy/econometrics-models/time-series-arima-models
Views: 92091 econometricsacademy
02417 Lecture 10 part A: Marima package in R for multivariate ARMA models
 
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This is part of the course 02417 Time Series Analysis as it was given in the fall of 2017 and spring 2018. The full playlist is here: https://www.youtube.com/playlist?list=PLtiTxpFJ4k6TZ0g496fVcQpt_-XJRNkbi You can download the slides here: https://drive.google.com/drive/folders/1OYamq8_PONteNHEdgkEG-jLvraeaGOp6?usp=sharing The course is based on the book: Time Series Analysis by Henrik Madsen: http://henrikmadsen.org/books/time-series-analysis/
Time Series with R - Part 1 - The Air Passnegers Data Set
 
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Time Series with R - Part 1 - The Air Passnegers Data Set
Jeffrey Yau - Time Series Forecasting using Statistical and Machine Learning Models
 
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PyData New York City 2017 Time series data is ubiquitous, and time series modeling techniques are data scientists’ essential tools. This presentation compares Vector Autoregressive (VAR) model, which is one of the most important class of multivariate time series statistical models, and neural network-based techniques, which has received a lot of attention in the data science community in the past few years.
Views: 16100 PyData
Visualization of Multivariate Time Series Data
 
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Visualization of Multivariate Time Series Data, using data from Capital Bike Share data as an example. Check it out at: https://sajudson.github.io/dataviz-project/
Views: 435 Scott Judson
Time Series Forecasting Theory | AR, MA, ARMA, ARIMA | Data Science
 
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In this video you will learn the theory of Time Series Forecasting. You will what is univariate time series analysis, AR, MA, ARMA & ARIMA modelling and how to use these models to do forecast. This will also help you learn ARCH, Garch, ECM Model & Panel data models. For training, consulting or help Contact : [email protected] For Study Packs : 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
Views: 308698 Analytics University
Time Series Plots in R
 
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This clip demonstrates how to use xts typed time-series data to create time-series plots in R using ggplot. The full documentation is on: http://eclr.humanities.manchester.ac.uk/index.php/R_TSplots and the data can be downloaded from: http://eclr.humanities.manchester.ac.uk/index.php/R#Intermediate_Techniques Table of Contents: 00:00 - Introduction 00:54 - Importing Data from csv 01:16 - Transforming to xts format 06:55 - Preparing data for ggplot 10:27 - A simple line graph 12:55 - Changing the plot 15:25 - Preparing for multiple grid plot 18:12 - Producing the multiple plot
Views: 26193 Ralf Becker
Time Series Analysis - 2 | Time Series in R | ARIMA Model Forecasting | Data Science | Simplilearn
 
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This Time Series Analysis (Part-2) in R tutorial will help you understand what is ARIMA model, what is correlation & auto-correlation and you will alose see a use case implementation in which we forecast sales of air-tickets using ARIMA and at the end, we will also how to validate a model using Ljung-Box text. Link to Time Series Analysis Part-1: https://www.youtube.com/watch?v=gj4L2isnOf8 You can also go through the slides here: https://goo.gl/9GGwHG 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. Introduction to ARIMA model 2. Auto-correlation & partial auto-correlation 3. Use case - Forecast the sales of air-tickets using ARIMA 4. Model validating using Ljung-Box test 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-Y5T3ZEMZZKs&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: 2386 Simplilearn
Forecasting Time Series Data in R | Facebook's Prophet Package 2017 & Tom Brady's Wikipedia data
 
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An example of using Facebook's recently released open source package prophet including, - data scraped from Tom Brady's Wikipedia page - getting Wikipedia trend data - time series plot - handling missing data and log transform - forecasting with Facebook's prophet - prediction - plot of actual versus forecast data - breaking and plotting forecast into trend, weekly seasonality & yearly seasonality components prophet procedure is an additive regression model with following components: - a piecewise linear or logistic growth curve trend - a yearly seasonal component modeled using Fourier series - a weekly seasonal component forecasting is an important tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 15987 Bharatendra Rai
R: Exploratory Data Analysis (EDA), Multivariate Analysis
 
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One of the first steps to data analysis is to perform Exploratory Data Analysis. In this video we go over the basics of multivariate data analysis, or analyzing the relationship between variables Here's the dataset used in this video: https://drive.google.com/open?id=0B67hcgV97X0mbnRYNzhYLU53X2c
Views: 5308 James Dayhuff
Understanding Basic Time Series Data in R
 
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Training on Understanding Basic Time Series Data in R by Vamsidhar Ambatipudi
Time Series Analysis (Georgia Tech) - 3.1.3 - Multivariate Time Series - Data Examples
 
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Time Series Analysis PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech Unit 3: Multivariate Time Series Modelling Part 1: Multivariate Time Series Lesson: 3 - Multivariate Time Series - Data Examples
Views: 95 Bob Trenwith
Multivariate Time Series Analysis with the VARMAX Procedure
 
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Xilong Chen presents using PROC VARMAX for time series analysis. SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 2404 SAS Software
Time Series Forecasting with LSTM Deep Learning
 
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A quick tutorial on Time Series Forecasting with Long Short Term Memory Network (LSTM), Deep Learning Techniques. The detailed Jupyter Notebook is available at https://anaconda.org/jaganadhg/eneryconsumeforecast_deeplearning/notebook
Views: 13267 Jaganadh Gopinadhan
Time Series in R Session 1.5 (Regression)
 
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Time Series in R, Session 1, part 5 (Ryan Womack, Rutgers University) http://libguides.rutgers.edu/data twitter: @ryandata
Views: 14746 librarianwomack
Two Effective Algorithms for Time Series Forecasting
 
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In this talk, Danny Yuan explains intuitively fast Fourier transformation and recurrent neural network. He explores how the concepts play critical roles in time series forecasting. Learn what the tools are, the key concepts associated with them, and why they are useful in time series forecasting. Danny Yuan is a software engineer in Uber. He’s currently working on streaming systems for Uber’s marketplace platform. This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl The next QCon is in New York, June 25-29, 2018. Check out the tracks and speakers: https://bit.ly/2JFHitG Save $100 with “INFOQ18” For more awesome presentations on innovator and early adopter topics, check InfoQ’s selection of talks from conferences worldwide http://bit.ly/2tm9loz Join a community of over 250 K senior developers by signing up for InfoQ’s weekly Newsletter: https://bit.ly/2wwKVzu
Views: 12036 InfoQ
Time Series Analysis with forecast Package in R Example Tutorial
 
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What is the difference between Autoregressive (AR) and Moving Average (MA) models? Explanation Video: https://www.youtube.com/watch?v=2kmBRH0caBA
Views: 12565 The Data Science Show
Jeffrey Yau | Applied Time Series Econometrics in Python and R
 
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PyData SF 2016 Time series data is ubitious, and time series statistical models should be included in any data scientists’ toolkit. This tutorial covers the mathematical formulation, statistical foundation, and practical considerations of one of the most important classes of time series models: the AutoRegression Integrated Moving Average with Explanatory Variables model and its seasonal counterpart. Time series data is ubitious, both within and out of the field of data science: weekly initial unemployment claim, tick level stock prices, weekly company sales, daily number of steps taken recorded by a wearable, just to name a few. Some of the most important and commonly used data science techniques to analyze time series data are those in developed in the field of statistics. For this reason, time series statistical models should be included in any data scientists’ toolkit. This 120-minute tutorial covers the mathematical formulation, statistical foundation, and practical considerations of one of the most important classes of time series models, AutoRegression Integrated Moving Average with Explanatory Variables (ARIMAX) models, and its Seasonal counterpart (SARIMAX).
Views: 28869 PyData
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: 68707 Jeromy Anglim
Multivariate Data - Data Analysis with R
 
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This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 2197 Udacity
R tutorial: xts & zoo for time series analysis
 
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Learn more about time series analysis with xts & zoo: https://www.datacamp.com/courses/manipulating-time-series-data-in-r-with-xts-zoo So, what is xts? xts stands for "eXtensible time series"; Objects that are designed to be flexible and powerful - designed to make using time series easy. At the heart of xts is a zoo object, a matrix object plus a vector of times corresponding to each row, which in turn represents an observation in time. Visually, you can think of this as data plus an array of times. To illustrate, we'll create a simple matrix called "x". Each row of our data is an observation in time. To track these observations we have dates in an object called "idx". Note that this index must be a true time object, not a string or number that looks like time. Now, xts lets you use nearly any time class - be it of class Date, POSIX times, timeDate, chron and more - but they need to be time based. Here we are using R's Date objects. At this point though we don't have a time series. We'll need to join these to create our xts object. To do this, we call the xts constructor with our data "x" and pass our dates "idx' to order.by. The constructor has a few optional arguments, the most useful being "tzone" - to set time zones and "unique", which will force all times be unique. Note that xts doesn't enforce uniqueness for your index, but you may require this in your own applications. One thing to note is that your index should be in increasing order of time. Earlier observations at the top of your object, and later more recent observations toward the bottom. If you pass in a non-sorted vector, xts will reorder your index and the corresponding rows of your data to ensure you have a properly ordered time series. Looking back to the example, you can see that we now have a matrix of values with dates on the left. They may look like rownames, but remember its really our index. So what makes xts special? As I mentioned before - xts is a matrix that has associated times for each observation. Basic operations work just like they would on a matrix, almost. One difference you'll note is that subsets will always preserve the object's 'matrix' form - choose one or more than one column will always results in another matrix object. Another difference is that attributes are generally preserved as you work with your data - so if you store something like a timestamp of when you acquired the data in an 'xts attribute' subsetting won't cause that information to be lost. Finally since xts is a subclass of zoo, you get all the power of zoo methods for free. We'll see how important this is throughout the course. One final point before we break out the exercises. Sometimes it will be necessary to reverse the steps we took to create the time series, and instead extract our raw data or raw times for use in other contexts. xts provides two functions that we'll cover here. coredata() is how you get the raw matrix back, and index() is how you extract the dates or times. Simple and effective. Now, let's get to work!
Views: 8607 DataCamp
ARIMAX | Time Series Model
 
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In this video you will learn about ARIMAX model and how is it different from the ARIMA class of model Analytic Study Pack - http://analyticuniversity.com/
Views: 9142 Analytics University
Time Series modelling using R | ARIMA, AR, MA, ARMA| Part-1
 
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In this video you will learn how to build an ARIMA model using R for stationary time series. You can also find AR, MA, ARIMA model theory on our channel do check it out. Contact [email protected]
Views: 22021 Analytics University
Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data
 
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Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data David Hallac (Stanford University) Sagar Vare (Stanford University) Stephen Boyd (Stanford University) Jure Leskovec (Stanford University) Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through an expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve both the E and M-steps in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios. More on http://www.kdd.org/kdd2017/
Views: 3930 KDD2017 video
Time Series Modelling and State Space Models: Professor Chris Williams, University of Edinburgh
 
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- AR, MA and ARMA models - Parameter estimation for ARMA models - Hidden Markov Models (definitions, inference, learning) - Linear-Gaussian HMMs (Kalman filtering) - More advanced topics (more elaborate state-space models, and recurrent neural networks)
Predictive Modelling Techniques | Data Science With R Tutorial
 
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This lesson will teach you Predictive analytics and Predictive Modelling Techniques. Watch the New Upgraded Video: https://www.youtube.com/watch?v=DtOYBxi4AIE After completing this lesson you will be able to: 1. Understand regression analysis and types of regression models 2. Know and Build a simple linear regression model 3. Understand and develop a logical regression 4. Learn cluster analysis, types and methods to form clusters 5. Know more series and its components 6. Decompose seasonal time series 7. Understand different exponential smoothing methods 8. Know the advantages and disadvantages of exponential smoothing 9. Understand the concepts of white noise and correlogram 10. Apply different time series analysis like Box Jenkins, AR, MA, ARMA etc 11. Understand all the analysis techniques with case studies Regression Analysis: • Regression analysis mainly focuses on finding a relationship between a dependent variable and one or more independent variables. • It predicts the value of a dependent variable based on one or more independent variables • Coefficient explains the impact of changes in an independent variable on the dependent variable. • Widely used in prediction and forecasting Data Science with R Language Certification Training: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-r-tools-training?utm_campaign=Predictive-Analytics-0gf5iLTbiQM&utm_medium=SC&utm_source=youtube #datascience #datasciencetutorial #datascienceforbeginners #datasciencewithr #datasciencetutorialforbeginners #datasciencecourse The Data Science with R training course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using R. The course is packed with real-life projects, case studies, and includes R CloudLabs for practice. Mastering R language: The 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. Mastering advanced statistical concepts: The course also includes the various statistical concepts like linear and logistic regression, cluster analysis, and forecasting. You will also learn hypothesis testing. As a part of the 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 Internet. R CloudLab has been provided to ensure a practical and hands-on experience. Additionally, we have four more projects for further practice. Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 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 For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 197916 Simplilearn
ARIMA Modelling and Forecasting in R
 
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Hello researchers, This video will help to learn how to fit and forecast AR, MA, ARMA, ARIMA models in R. You can visit my blog for the same: http://learningeconometrics.blogspot.in/2016/09/arima-modelling-in-r.html
Views: 18256 Sarveshwar Inani
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: 725133 Jalayer Academy
R - Exploring Data (part 5) - Multivariate Summaries
 
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We explore some multivariate descriptive tools here. Scatterplot matrix, side-by-side boxplot, two-way crosstab, correlation matrix, and more.
Views: 9293 Jalayer Academy
Tidy forecasting in R
 
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The conventional matrix structure that underlies time series models in R does not easily accommodate a few complications, such as multiple variables, heterogeneous data types, low time resolutions, implicit missing values, and multilevel. This work addresses the broader issues of better data structures and modern data pipelines for analysing and visualising temporal-context data. We extend the tidy data concept to temporal data, and note that the “molten” data structure is flexible enough to handle heterogeneity, low time resolutions, and implicit missing values. There are two constraints required to turn the “molten” data into a valid temporal data: (1) an explicitly declared index variable containing timestamps; (2) a constraint uniquely identifies the multiple units of measurements, which is referred to as a “key”. A syntactical approach is introduced to describe nested or crossed data structure, which employs the “key”. Based on the tidy temporal data, a data pipeline is discussed and formulated to facilitate time-based transformation and visualisation. A case study is included to demonstrate the tidy structure and the data pipeline ideas and usage.
Views: 1229 R Consortium
Time Series Analysis (Georgia Tech) - 3.1.1 Multivariate Time Series - Introduction and Examples
 
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Time Series Analysis PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech Unit 3: Multivariate Time Series Modelling Part 1: Multivariate Time Series Lesson: 1 Multivariate Time Series - Introduction and Examples
Views: 92 Bob Trenwith
Time Series modelling using R | ARIMA, AR, MA, ARMA  | Non Stationary Series|Part2
 
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In this video you will learn how to build time series ARIMA model using R for non-stationary series. Contact [email protected]
Views: 8143 Analytics University
Time Series in R Session 1.3 (Forecasting)
 
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Time Series in R, Session 1, part 3 (Ryan Womack, Rutgers University) http://libguides.rutgers.edu/data twitter: @ryandata
Views: 30204 librarianwomack
Combining Multivariate Time Series and Derivatives Analytics
 
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Yves Hilpisch illustrates the use of advanced Vector Autoregression techniques to forecast parameter time series of option pricing models. He uses the Python-based financial analytics library http://dx-analytics.com to implement the derivatives pricing.
Views: 737 Yves Hilpisch
Multivariate Time Series Analysis of Physiological and Clinical Data
 
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University of Puerto Rico, Medical Sciences Campus Multivariate Time Series Analysis of Physiological and Clinical Data Dr. Patricia Ordóñez Rozo Assistant Professor,Department of Computer Science College of Natural Sciences University of Puerto Rico Río Piedras Campus
Time Series in R Session 1.6 (Stationary and Non-Stationary Models)
 
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Time Series in R, Session 1, part 6 (Ryan Womack, Rutgers University) http://libguides.rutgers.edu/data twitter: @ryandata
Views: 28098 librarianwomack
Online Clustering of Multivariate Time Series
 
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Supplementary materials for SDM 2016 submission
Views: 779 OEC
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: 21003 Hvass Laboratories
Introduction To Time Series In R Basic Models
 
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In this video we will be discussing some of the basic models R has in the forecasting package. This includes the average or mean method, the naive method, the seasonal naive method and the drift method. These four forecasting models are a great introduction into the world of predictive modeling. We will discuss them on a conceptual level and then demo how you can use them in R. Please feel free to reach out to us if you have any questions. http://www.acheronanalytics.com/contact.html
Views: 678 Ben R