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Time series in hindi and simple language
 
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Thank you friends to support me Plz share subscribe and comment on my channel and Connect me through Instagram:- Chanchalb1996 Gmail:- [email protected] Facebook page :- https://m.facebook.com/Only-for-commerce-student-366734273750227/ Unaccademy download link :- https://unacademy.app.link/bfElTw3WcS Unaccademy profile link :- https://unacademy.com/user/chanchalb1996 Telegram link :- https://t.me/joinchat/AAAAAEu9rP9ahCScbT_mMA
Views: 26302 study with chanchal
Introducing Time Series Data
 
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(Index: https://www.stat.auckland.ac.nz/~wild/wildaboutstatistics/ ) We’ll learn to plot series of data against time and use techniques that ‘pull apart’ our plots to help identify patterns. After you’ve watched this video, you should be able to answer these questions •What is time-series data? •Why are people interested in time-series data? •What is quarterly data? •Why do people plot time-series data with points joined up by lines instead of using normal scatterplots? •What, besides trends, is another form of pattern that is very common in time-series data
Views: 15285 Wild About Statistics
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: 88007 edureka!
TIME SERIES ANALYSIS THE BEST EXAMPLE
 
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QUANTITATIVE METHODS TIME SERIES ANALYSIS
Views: 210524 Adhir Hurjunlal
Chapter 16: Time Series Analysis (1/4)
 
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Time Series Analysis: Introduction to the model; Seasonal Adjustment Method Part 1 of 4
Views: 187037 Simcha Pollack
Time Series Prediction
 
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Time series is the fastest growing category of data out there! It's a series of data points indexed in time order. Often, a time series is a sequence taken at successive equally spaced points in time. In this video, I'll cover 8 different time series techniques that will help us predict the price of gold over a period of 3 years. We'll compare the results of each technique, and even consider using a learning technique. From Holts Winter Method to Vector Auto Regression to Reinforcement Learning, we've got a lot to cover here. Enjoy! Code for this video: https://github.com/llSourcell/Time_Series_Prediction Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://www.altumintelligence.com/articles/a/Time-Series-Prediction-Using-LSTM-Deep-Neural-Networks https://blog.statsbot.co/time-series-prediction-using-recurrent-neural-networks-lstms-807fa6ca7f https://towardsdatascience.com/bitcoin-price-prediction-using-time-series-forecasting-9f468f7174d3 https://www.datascience.com/blog/time-series-forecasting-machine-learning-differences https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/ https://www.youtube.com/watch?v=hhJIztWR_vo Join us at School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: www.theschool.ai/jobs/ Need help on a project? See our consulting group: www.theschool.ai/consulting-group/ Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 63678 Siraj Raval
Time Series Data Mining Forecasting with Weka
 
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I am sorry for my poor english. I hope it helps you. when i take the data mining course, i had searched it but i couldnt. So i decided to share this video with you.
Views: 25653 Web Educator
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: 406336 Analytics University
Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka
 
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** 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: 83805 edureka!
Time Series Analysis
 
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This is Lecture series on Time Series Analysis Chapter of Statistics. In this part, you will learn the meaning of time series and its analysis. Watch all statistics videos at http://svtuition.com/watch/#ST
Views: 31554 Svtuition
Performance 1: Data partitioning for time series
 
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Data partitioning is a fundamental step in predictive modeling. For time series, partitioning is done differently from cross-sectional data. This video supports the textbook Practical Time Series Forecasting. http://www.forecastingbook.com http://www.galitshmueli.com
Views: 4780 Galit Shmueli
Maths Tutorial: Smoothing Time Series Data (statistics)
 
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VCE Further Maths Tutorials. Core (Data Analysis) Tutorial: Smoothing Time Series Data. This tute runs through mean and median smoothing, from a table and straight onto a graph, using 3 and 5 mean & median smoothing and 4 point smoothing with centring. For more tutorials, visit www.vcefurthermaths.com
Views: 58790 vcefurthermaths
Time Series data Mining Using the Matrix Profile part 1
 
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Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins Part 1 Authors: Abdullah Al Mueen, Department of Computer Science, University of New Mexico Eamonn Keogh, Department of Computer Science and Engineering, University of California, Riverside Abstract: The Matrix Profile (and the algorithms to compute it: STAMP, STAMPI, STOMP, SCRIMP and GPU-STOMP), has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability. In particular it has implications for time series motif discovery, time series joins, shapelet discovery (classification), density estimation, semantic segmentation, visualization, clustering etc. Link to tutorial: http://www.cs.ucr.edu/~eamonn/MatrixProfile.html More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 2953 KDD2017 video
Data Mining and Prediction Modelling in the Dairy Industry Using Time Series and Sliding Windows
 
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"WHY - As a major livestock producer, the European Union is directly affected by the global need for more sustainable food production. Climate change will undoubtedly impact on farm animal production but the health and welfare of livestock is also of increasing public concern. Due to rapid development of precision livestock farming technologies and availability of high-throughput from milk sensors, large-scale massive data has become available on research farms. The preferred matrix to measure the biomarkers is milk, as it is more accessible than blood and allows low-cost, automated repeat sampling using ‘in-line’ sampling and analytical technologies. WHAT - Certain biomarkers in milk such as N-glycan structures (BM-1), metabolites (BM-2) or mid-infra-red spectra (BM-3) can serve as biomarkers to predict production efficiency and disease. Data mining and machine learning can unlock insights around such biomarkers. As more of the aforementioned types of datasets become available over the near future, scalable data mining and prediction pipelines applied to animals science are needed. TAKEAWAYS -In this session you will learn: The methodology for ranking multiple biomarkers according to their predictive power; Data processing and statistical modelling performed using Spark v2.1.1 with scala API; Infrastructure, configuration, and implementation of the data pipeline using sliding windows with Apache Spark’s MLlib Visualization of of datasets via ElasticSearch-Kibana. Talk by Miel Hostens Session hashtag: #EUds14"
Views: 511 Databricks
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 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: 48333 InfoQ
Rearchitecting a SQL Database for Time-Series Data | DataEngConf NYC '17
 
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WANT TO EXPERIENCE A TALK LIKE THIS LIVE? Barcelona: https://www.datacouncil.ai/barcelona New York City: https://www.datacouncil.ai/new-york-city San Francisco: https://www.datacouncil.ai/san-francisco Singapore: https://www.datacouncil.ai/singapore ABOUT THE TALK: Today everything is instrumented, generating more and more time-series data streams that need to be monitored and analyzed. When it comes to storing this data, many developers start with some well-trusted system like PostgreSQL. But when their data hits a certain scale, they often give up its query power and ecosystem by migrating to some NoSQL or other "modern" time-series architecture. In this talk, I describe why this perceived trade-off isn't necessary, and how we've built an efficient, scalable time-series database engineered up from PostgreSQL. In particular, the nature of time-series workloads one finds in devops, monitoring, IoT, finance, and elsewhere -- inserting new data about recent events -- presents very different demands than general transactional (OLTP) workloads. We've architected our time-series database to take advantage of and embrace these differences. The system architecture automatically partitions data across both time and space, even though it exposes the illusion of a single continuous table -- a hypertable -- across all of your data spread across one or many servers. Its distributed query optimizations both hide the fact that users are interacting with many "chunks" of data, which are right-sized by volume and time constraints, and minimize which and how chunks are accessed to answer queries. In fact, the database supports "full SQL" against this hypertable (e.g., secondary indexes, rich query predicates and group bys, aggregations, windowing functions, upserts, CTEs, JOINs). Through performance benchmarks, I show how the database scales much better than PostgreSQL, even on a single node. In particular, it avoids the "performance cliff" that vanilla PostgreSQL experiences at 10s of millions of rows, while maintaining robust performance past 100B rows. The database is implemented as a PostgreSQL extension, released under the Apache 2 license. ABOUT THE SPEAKER: Michael J. Freedman is a Professor in the Computer Science Department at Princeton University, as well as the co-founder and CTO of Timescale, building an open-source database that scales out SQL for time-series data. His work broadly focuses on distributed systems, networking, and security, and has led to commercial products and deployed systems reaching millions of users daily. Honors include a Presidential Early Career Award (PECASE), SIGCOMM Test of Time Award, Sloan Fellowship, DARPA CSSG membership, and multiple award publications. FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai Facebook: https://www.facebook.com/datacouncilai
Views: 3173 Data Council
Forecasting with the Microsoft Time Series Data Mining Algorithm
 
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Imagine taking historical stock market data and using data science to more accurately predict future stock values. This is precisely the aim of the Microsoft Time Series data mining algorithm.. MSBI - SSAS - Data Mining - Time Series. In this video you will learn the theory of Time Series Forecasting. You will what is univariate time series analysis, AR, MA, ARMA vesves ARIMA modelling and how to use these models to do forecast.. I am sorry for my poor english. I hope it helps you. when i take the data mining course, i had searched it but i couldnt. So i decided to share this video with you.
Views: 863 Fidela Aretha
Machine Learning for Time Series Data in Python | SciPy 2016 | Brett Naul
 
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The analysis of time series data is a fundamental part of many scientific disciplines, but there are few resources meant to help domain scientists to easily explore time course datasets: traditional statistical models of time series are often too rigid to explain complex time domain behavior, while popular machine learning packages deal almost exclusively with 'fixed-width' datasets containing a uniform number of features. Cesium is a time series analysis framework, consisting of a Python library as well as a web front-end interface, that allows researchers to apply modern machine learning techniques to time series data in a way that is simple, easily reproducible, and extensible.
Views: 43559 Enthought
Excel at Data Mining - Time Series Forecasting
 
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In this video, Billy Decker of StatSlice Systems shows you how to start data mining in 5 minutes with the Microsoft Excel data mining add-in*. In this example, we will create a forecasting model that will predict the trend of bikes sales in different regions. For the example, we will be using a tutorial spreadsheet that can be found on Codeplex at: https://dataminingaddins.codeplex.com/releases/view/87029 *This tutorial assumes that you have already installed the data mining add-in for Excel and configured the add-in to be pointed at an instance of SQL Server to which you have access rights.
Views: 4837 StatSlice Systems
Time-Series Analysis with R | Clustering
 
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Provides steps for carrying out time-series analysis with R and covers clustering stage. Previous video - time-series forecasting: https://goo.gl/wmQG36 Next video - time-series classification: https://goo.gl/w3b55p Time-Series videos: https://goo.gl/FLztxt Machine Learning videos: https://goo.gl/WHHqWP Becoming Data Scientist: https://goo.gl/JWyyQc Introductory R Videos: https://goo.gl/NZ55SJ Deep Learning with TensorFlow: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi Text mining: https://goo.gl/7FJGmd Data Visualization: https://goo.gl/Q7Q2A8 Playlist: https://goo.gl/iwbhnE 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: 1410 Bharatendra Rai
MSBI - SSAS - Data Mining - Time Series
 
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MSBI - SSAS - Data Mining - Time Series
Views: 880 M R Dhandhukia
Time Series data Mining Using the Matrix Profile part 2
 
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Time Series data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins Part 2 Authors: Abdullah Al Mueen, Department of Computer Science, University of New Mexico Eamonn Keogh, Department of Computer Science and Engineering, University of California, Riverside Abstract: The Matrix Profile (and the algorithms to compute it: STAMP, STAMPI, STOMP, SCRIMP and GPU-STOMP), has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability. In particular it has implications for time series motif discovery, time series joins, shapelet discovery (classification), density estimation, semantic segmentation, visualization, clustering etc. Link to tutorial: http://www.cs.ucr.edu/~eamonn/MatrixProfile.html More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 1231 KDD2017 video
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: 67900 Hvass Laboratories
Advanced Data Mining with Weka (1.4: Looking at forecasts)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 4: Looking at forecasts http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/JyCK84 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 5448 WekaMOOC
Time-Series Analysis with R | Classification
 
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Provides steps for carrying out time-series analysis with R and covers classification stage. Previous video - time-series clustering: https://goo.gl/UwsTxQ R code file: https://goo.gl/orX2YM Time-Series videos: https://goo.gl/FLztxt Machine Learning videos: https://goo.gl/WHHqWP Becoming Data Scientist: https://goo.gl/JWyyQc Introductory R Videos: https://goo.gl/NZ55SJ Deep Learning with TensorFlow: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi Text mining: https://goo.gl/7FJGmd Data Visualization: https://goo.gl/Q7Q2A8 Playlist: https://goo.gl/iwbhnE 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: 1073 Bharatendra Rai
Tutorial on Time Series Data Mining (Thai)
 
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นำมาจาก "Tutorial on Time Series Data Mining" โดย Thanawin Rakthanmanon Slides is adopted from VLDB2006 slides by Prof. Eamonn Keogh
Views: 1273 5argon
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: 20162 Jason Delaney
Time Series Classification Using Wavelet Scattering Transform
 
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This is a ~3-minute video highlight produced by undergraduate students Charlie Tian and Christina Coley regarding their research topic during the 2017 AMALTHEA REU Program at Florida Institute of Technology in Melbourne, FL. They were mentored by doctoral student Kaylen Bryan and professor Dr. Adrian Peter (Engineering Systems Department). More details about their project can be found at http://www.amalthea-reu.org.
Classifying and Clustering Data with R : Time Series Decomposition with R  | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2xQrLB8]. This video shows how to do time series decomposition in R. • Discuss an example of time series data • Show how to do log transformation of data • Show how to do decomposition of additive time series For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 5411 Packt Video
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: 23298 Bharatendra Rai
Time Series: Measurement of Trend in Hindi under E-Learning Program
 
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It covers in detail various methods of measuring trend like Moving Averags & Least Square. Lecture by: Rajinder Kumar Arora, Head of Department of Commerce & Management
Time-Series Analysis with R | Decomposition
 
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Provides steps for carrying out time-series analysis with R and covers decomposition stage. Next video - Time-Series Forecasting: https://goo.gl/o6uh67 Time-Series videos: https://goo.gl/FLztxt Machine Learning videos: https://goo.gl/WHHqWP Becoming Data Scientist: https://goo.gl/JWyyQc Introductory R Videos: https://goo.gl/NZ55SJ Deep Learning with TensorFlow: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi Text mining: https://goo.gl/7FJGmd Data Visualization: https://goo.gl/Q7Q2A8 Playlist: https://goo.gl/iwbhnE 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: 2242 Bharatendra Rai
Introduction to Time Series Forecasting [AAT-202]
 
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Speaker(s): Peter Myers Imagine taking historical stock market data and using data science to more accurately predict future stock values. This is precisely the aim of the Microsoft Time Series data mining algorithm. Of course, your objective doesn't need to be personal profit to attend this session! SQL Server Analysis Services includes the Microsoft Time Series algorithm to provide an approach to intuitive and accurate time series forecasting. The algorithm can be used in scenarios where you have a historic series of data and where you need to predict a future series of values based on more than just your gut instinct. This session will describe how to prepare data, create and query time series data mining models, and interpret query results. Various demonstration data mining models will be created by using Visual Studio and, in self-service scenarios, by using the data mining add-ins available in Excel.
Views: 462 PASStv
Time Series - 16 Method of Moving Averages - Even period cycle - Centred Moving Average
 
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#Statistics #Time #Series #Business #Forecasting #Moving #Average #Weighted #Centered #Trend #Values #Even Method of Moving Averages Method of moving averages is a very simple and flexible method of measuring trend. It consists in obtaining a series of moving averages (arithmetic means) of successive overlapping groups or sections of the time series. The averaging process smoothens out fluctuations and the ups and downs in the given data. The moving average of period ‘M’ is a series of successive averages (arithmetic means) of M overlapping values at a time, starting with first, second, third value and so on. Thus, for a time series, values y1, y2, y3, y4,… for different time periods the moving averages (MA) values of period M are given below 1st M.A. = (y1 + y2 + … yM) / M 2nd M.A. = (y2 + y3 + … yM + 1) / M 3rd M.A. = (y3 + y4 + … yM + 2) / M When period is odd If the period ‘M’ of the moving average is odd, then the successive value of the moving averages are placed against the middle value of the corresponding time intervals. When period is even If the period ‘M’ of the moving average is even then there are two middle periods and the moving average values are placed in between the two middle periods of the time intervals it covers. In this case the moving average values will not co-inside with a period of the given time series and attempt is made to synchronize them with the original data by taking two periods average of moving average and placing them in between the corresponding time periods. This technique is called centering and the corresponding moving average values are called centered moving average. Merits and Demerits of moving average Method Merits 1) This method does not require any mathematical complexities and is quite simple to understand and use as compared with the principle of least squares method. 2) This method does not involve any element of subjectivity, since the choice of the period of moving average is determined by oscillatory movements in the data and not by the personal judgement of the investigation. 3) Unlike the method of trend fitting by the principle of least squares the moving average method is quite flexible in the sense that a few more observations may be added to the given data without affecting the trend values already obtained. The addition of some new observations will simply result in some more trend values at the end. 4) In addition to the measurement of trend, the method of moving averages is also useful for measurement of seasonal, cyclical and irregular fluctuations. Limitations 1) An obvious limitation of this method is that we cannot obtain the trend values for all the given observations. We have to for go the trend values for some observations of both the extremes depending on the period of moving average. 2) Since the trend values obtained by the method of moving averages cannot be expressed by any functional relationship, this method cannot be used for forecasting or predicting future values which is the main objective of trend analysis. 3) The selection of the period of moving average is very important and is not easy to determine particularly when the time series does not exhibit cycles, which are regular in period. 4) In case of a non-linear trend, which is generally the case in most economic and business time series, the trend values given by the method of moving average are biased and they lie either above or below the true swipe of the data. Case Find out 4 years’ centred moving averages. Year 1 2 3 4 5 6 7 8 9 10 Value 430 470 450 460 480 470 470 500 490 480 Time Series, Business Forecasting, Method of Moving Averages, Centred Moving Average, Trend Value, 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: 51288 Prashant Puaar
Predictive Analytics using Orange Data Mining
 
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Data Mining Fruitful and Fun Open source machine learning and data visualization for novice and expert. Interactive data analysis workflows with a large toolbox. Download Link: https://orange.biolab.si/download/
Views: 4090 Anurag P
Predicting Stock Prices with SSAS Mining Models
 
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Predictive analytics and supervised machine learning with SSAS and C#. In this demo I use MS Time Series Mining structure within SSAS to predict stock prices using the Auto Regressive Integrated Moving Average (ARIMA) method. This is a bit of supervised machine learning with analysis services. I then query the mining model with SSMS and run a prediction query from a C# applications
Views: 3550 sackdeezle
Algorithms (Time Series Segmentation) | Medical Data Mining L01T05 | Introduction & Scientific Know.
 
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The Online Certificate Program in Genomics and Biomedical Informatics Bar-Ilan University & Sheba Medical Center Course 803.80-675 - Medical Data Mining Spring 2018 Lecturer: Dr. Ronen Tal-Botzer [email protected] Unit L01: Introduction & Scientific Knowledge Topic T05: Algorithms (Time Series Segmentation)
Time-Series Analysis with R | Forecasting
 
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Provides steps for carrying out time-series analysis with R and covers forecasting stage. Previous video - time-series decomposition: https://goo.gl/hRJmU1 Next video - time-series clustering: https://goo.gl/5gMryj Time-Series videos: https://goo.gl/FLztxt Machine Learning videos: https://goo.gl/WHHqWP Becoming Data Scientist: https://goo.gl/JWyyQc Introductory R Videos: https://goo.gl/NZ55SJ Deep Learning with TensorFlow: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi Text mining: https://goo.gl/7FJGmd Data Visualization: https://goo.gl/Q7Q2A8 Playlist: https://goo.gl/iwbhnE 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: 1504 Bharatendra Rai
สอนทำ Data Mining ด้วย Excel: การพยากรณ์อนุกรมเวลา (Time series forecasting) ตอนที่ 1
 
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ทดสอบ Microsoft Time Series Algorithm ที่ใช้ในการพยากรณ์อนุกรมเวลา (Time series forecasting) โดยใช้ SQL Server Data Mining Add-in สำหรับ Excel เนื้อหาจะประกอบไปด้วยการพยากรณ์ข้อมูลอนุกรมเวลาโดยจำลองข้อมูลเป็น 3 ชนิด ได้แก่ 1) เชิงเส้น (linear) 2) ไม่เชิงเส้น (non-linear) 3) สุ่ม (random)
Views: 5966 prasertcbs
Time Series Forecasting Example in RStudio
 
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Demonstrates the forecasting process with a business example - the monthly dollar value of retail sales in the US from 1992-2017. Link to Hyndman and Athanasopoulos: https://otexts.org/fpp2/
Views: 4648 Adam Check
SAXually Explicit Images: Data Mining Large Shape Databases
 
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Google TechTalks May 12, 2006 Eamonn Keogh ABSTRACT The problem of indexing large collections of time series and images has received much attention in the last decade, however we argue that there is potentially great untapped utility in data mining such collections. Consider the following two concrete examples of problems in data mining. Motif Discovery (duplication detection): Given a large repository of time series or images, find approximately repeated patterns/images. Discord Discovery: Given a large repository of time series or images, find the most unusual time series/image. As we will show, both these problems have applications in fields as diverse as anthropology, crime prevention, zoology and entertainment. Both problems are trivial to solve given time quadratic in the number of objects, but only a linear time solution is tractable for realistic problems. In this talk we will show how a symbolic representation of the data call SAX (Symbolic Aggregate ApproXimation) allows fast, scalable solutions to these problems. Google engEDU
Views: 4669 GoogleTalksArchive
Searching and mining trillions of time series subsequences under dynamic time warping (KDD 2012)
 
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Searching and mining trillions of time series subsequences under dynamic time warping KDD 2012 Thanawin Rakthanmanon Bilson Campana Abdullah Mueen Gustavo Batista Brandon Westover Qiang Zhu Jesin Zakaria Eamonn Keogh Most time series data mining algorithms use similarity search as a core subroutine, and thus the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms. The difficulty of scaling search to large datasets largely explains why most academic work on time series data mining has plateaued at considering a few millions of time series objects, while much of industry and science sits on billions of time series objects waiting to be explored. In this work we show that by using a combination of four novel ideas we can search and mine truly massive time series for the first time. We demonstrate the following extremely unintuitive fact; in large datasets we can exactly search under DTW much more quickly than the current state-of-the-art Euclidean distance search algorithms. We demonstrate our work on the largest set of time series experiments ever attempted. In particular, the largest dataset we consider is larger than the combined size of all of the time series datasets considered in all data mining papers ever published. We show that our ideas allow us to solve higher-level time series data mining problem such as motif discovery and clustering at scales that would otherwise be untenable. In addition to mining massive datasets, we will show that our ideas also have implications for real-time monitoring of data streams, allowing us to handle much faster arrival rates and/or use cheaper and lower powered devices than are currently possible.
Temporal analysis: Generating time series from events based data
 
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Often data is captured in a different format than required for analysis. Have you ever needed to perform historical analysis on events-based data? For example, how do you calculate turnover based on employees' start and end dates? Or, if sensor data captures when a device switches between on, off, and idle, how do you calculate the percent of time that a device was active per period? Join this Jedi session to find out!
Views: 934 Tableau Software
Shaplets, Motifs and Discords: A set of Primitives for Mining Massive Time Series and Image Archives
 
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The past decade has seen tremendous interest in mining of time series and shape datasets, as such data can be found in domains as diverse as entertainment, finance, medicine and astronomy. However, much of this work has focused on toy problems, with a few thousand objects. In recent years, our research group has made an effort to address the problems of classification, clustering, query-by-content, motif discovery, and outlier detection on truly massive datasets, with 100 million-plus objects. In this talk we will summarize our research findings over the last two years, and show that a small set of primitives, shaplets, motifs and discords, allow us to solve essentially all problems in shape/time series data mining with efficient, effective and interpretable results. We will demonstrate the utility of our ideas, with case studies in anthropology, astronomy, entomology, historical manuscript annotation and medicine.
Views: 671 Microsoft Research
Introduction to Data Mining in SQL Server Analysis Services
 
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Data mining is one of the key hidden gems inside of Analysis Services but has traditionally had a steep learning curve. In this session, you'll learn how to create a data mining model to predict who is the best customer for you and learn how to use other algorithms to spend your marketing model wisely. You'll also see how to use Time Series analysis for budget and forecast prediction. Finally, you'll learn how to integrate data mining into your application through SSIS or custom coding.
Views: 12245 PASStv
Time Series Data - Analysis with the Aggregation Framework
 
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This is a clip from our recent webinar, Time Series Data. To view the full presentation, click here: http://www.mongodb.com/presentations/mongodb-time-series-data The webinar asks you to imagine that self-driving cars now exist and are becoming widespread around the world. To facilitate the transition, it's necessary to set up central service to monitor traffic conditions nationwide, deploy sensors throughout the interstate system that monitor traffic conditions including car speeds, pavement and weather conditions, as well as accidents, construction, and other sources of traffic tie ups. MongoDB has been selected as the database for this application. In this webinar, we will walk through designing the application’s schema that will both support the high update and read volumes as well as the data aggregation and analytics queries.
Views: 3545 MongoDB