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Introduction to Time Series Analysis | Statistics | Mathematics | Mathur Sir Classes
 
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Introduction to Time Series Analysis | Statistics | Mathematics | Mathur Sir Classes #MathurSirClasses #StudyMaterial If you like this video and wish to support this EDUCATION channel, please contribute via, * Paytm a/c : 9830489610 * Paypal a/c : www.paypal.me/mathursirclasses [Every contribution is helpful] Thanks & All the Best WE NEED YOUR SUPPORT TO GROW UP..SO HELP US!! Hope you guys like this one. If you do, please hit Like!!! Please Share it with your friends! Thank You! Please SUBSCRIBE for more videos. Music - www.bensound.com Video Recording and Editing by - Gyankaksh Educational Institute (9051378712) https://www.youtube.com/channel/UCFzUEzxnRDsbWIA5rnappwQ
Views: 3569 Mathur Sir Classes
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: 3299 study with chanchal
TIME SERIES ANALYSIS THE BEST EXAMPLE
 
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QUANTITATIVE METHODS TIME SERIES ANALYSIS
Views: 195047 Adhir Hurjunlal
Time Series Analysis in SPSS
 
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SPSS training on Conjoint Analysis by Vamsidhar Ambatipudi
Views: 30763 Vamsidhar Ambatipudi
yt workshop 2012 - Time Series Analysis
 
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http://yt-project.org/workshop2012/ Time Series Analysis by Britton Smith Slides: https://bitbucket.org/brittonsmith/yt.workshop2012.time-series/src/tip/output/time_series.pdf Repository: https://bitbucket.org/brittonsmith/yt.workshop2012.time-series/
Views: 151 yt Project
Time Series - 1 Method of Least Squares - Fitting of Linear Trend - Odd number of years
 
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#Statistics #Time #Series #Business #Forecasting #Linear #Trend #Values #LeastSquares #Fitting #Odd 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 Linear Trend Let the straight line trend between the given time series values (y) and time (x) be given by the standard equation: y = a + bx Then for any given time ‘x’ the estimated value of ye as given by the equation is ye = a + bx The following two normal equations are used for estimating 'a' and 'b'. Σy = na + bΣx Σxy = aΣx + bΣx^2 When Odd No. of Years, [X = (Year – Origin) / Interval] Case Given below are the figures of sales (in '000 units) of a certain shop. Fit a straight line by the method of least square and show the estimate for the year 2017: Year: 2010 2011 2012 2013 2014 2015 2016 Sales: 125 128 133 135 140 141 143 Time Series, Linear 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: 87271 Prashant Puaar
Lecture - 35 The Analysis of Time Series
 
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Lecture series on Project and Production Management by Prof. Arun kanda, Department of Mechanical Engineering, IIT Delhi. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 140802 nptelhrd
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
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: 24765 PyData
Time Series Analysis: What is Stationarity?
 
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In this video you will learn what is a stationary series. It is an important property for AR, MA, ARIMA, Arch, Garch 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: 36739 Analytics University
Time Series | Statistics by CA Raj K Agrawal
 
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To Buy Complete Classes visit www.studyathome.org or Call: 8737012345. StudyAtHome.org is a Online Platform, that provides CA/ CS/ CMA classes from India's Best Professors at your Home.
Views: 22577 Study At Home
ARIMA modeling (video 1) in SPSS: model identification
 
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Be sure to visit my website at: https://sites.google.com/view/statistics-for-the-real-world/home This video is the first of several on ARIMA modeling using IBM SPSS. Specifically, it focuses on how to identify AR and MA processes. It also covers the topic of stationarity and identification of trending. (Be sure to check out the next video in the series on estimating ARIMA model parameters using SPSS syntax. Example syntax can be accessed through links in the video description) A copy of the original dataset can be downloaded here: https://drive.google.com/open?id=1gT2FbgUeZHIAG5vKctUrJWM--pbkXWRk The demonstrations provided in this video come from Chapter 18 of Tabachnick & Fidell's text, Using Multivariate Statistics (6th edition; https://www.pearson.com/us/higher-education/program/Tabachnick-Using-Multivariate-Statistics-6th-Edition/PGM332849.html) The chapter is downloadable from the textbook website at: http://media.pearsoncmg.com/ab/ab_tabachnick_multistats_6/datafiles/M18_TABA9574_06_SE_C18.pdf For more details of the computations involved, you can go here: https://youtu.be/WlSz0Ji19PM
Views: 11815 Mike Crowson
Mod-02 Lec-02 Forecasting -- Time series models -- Simple Exponential smoothing
 
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Operations and Supply Chain Management by Prof. G. Srinivasan , Department of Management Studies, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 197602 nptelhrd
Time Series Analysis with Python Intermediate | SciPy 2016 Tutorial | Aileen Nielsen
 
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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: 59301 Enthought
Time Series Analysis Webinar by Statgraphics
 
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Statgraphics: Time Series Analysis Webinar - This webinar deals with the analysis of sequential time series data. It covers the Descriptive Methods, Smoothing, Seasonal Decomposition, and Forecasting procedures. Special emphasis will be given to the use of Statgraphics' automatic model-selection methods for forecasting both seasonal and nonseasonal data. To access the slide presentation PDF and/or associated data files, please visit: http://www.statgraphics.com/webinars.
Views: 3075 Statgraphics
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: 4831 WekaMOOC
Working with Time Series Data in MATLAB
 
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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: 45491 MATLAB
Forecasting - Simple moving average - Example 1
 
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In this video, you will learn how to find out the 3 month and 4 monthly moving average for demand forecasting.
Views: 192121 maxus knowledge
Multilevel Time Series Analysis,  Mplus Short Course Topic 12, Part 2
 
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Mplus Short Course Topic 11: Regression and Mediation Analysis Part 9 - Missing Data Analysis Link to handouts associated with this segment (slides 13-22): http://www.statmodel.com/download/Part%201%20and%202%20Hamaker.pdf NOTE: For more information or to engage in discussion about the topics covered in this video, please visit www.statmodel.com.
Views: 100 Mplus
Malware Analysis   Quick PDF Analysis
 
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Ring Ø Labs report and sample download here: http://www.ringzerolabs.com/2017/08/we-show-how-to-quickly-analyze.html Ring Ø Labs Analysis Environment Setup: https://www.youtube.com/edit?o=U&video_id=Onqql1Zz3OE Ring Ø Labs is a Reverse Engineering site dedicated to analyzing malware, researching emergent security topics, and hacking the planet. www.RingZeroLabs.com Here are some approaches in reverse engineering a malware sample: Reverse engineer: The most obvious approach is to completely reverse engineer a piece of malware. This obviously takes a great amount of time, so other approaches are more practical. Exploitation techniques: Another approach you can take is to focus on the exploitation techniques of a piece of malware. Occasionally you will see a piece of malware that is using a new exploitation technique, or is exploiting a zero-day vulnerability. In this case you may be interested only in the specific exploitation technique so you can timebox your analysis and only look at the exploitation mechanisms. Obfuscation: Malware will often obfuscate itself and make itself difficult to analyze. You might come across malware that you have seen before without obfuscation. In that case you may only want to focus on reverse engineering the new parts. Encryption methods: A common type of malware these days is ransomware. Ransomware essentially encrypts the victim's files and locks them up so that they can't be accessed or read. Oftentimes the authors of ransomware will make mistakes when they implement the encryption mechanisms. So if you focus your research on the encryption mechanisms you might be able to find weaknesses in their implementation and/or you might be able to find hard-coded keys or weak algorithms. C&C communication: This is something that is pretty commonly done when looking at malware. Analysts often want to figure out what the communication protocol is between a piece of malware on the client's side and the server on the command and control side. The communication protocol can actually give you a lot of hints about the malware’s capabilities. Attribution: Murky area - kind of like a dark art. It usually involves a lot of guesswork, knowledge of malicious hacking teams and looking at more than one piece of malware. Categorization and clustering: You can reverse engineer malware from a broader point of view. This involves looking at malware in bulk and doing a broad-stroke analysis on lots of different malware, rather than doing a deep dive. Techniques Now, let’s look at techniques that can be utilized while analyzing malware. First of all, we use static analysis. This is the process of analyzing malware or binaries without actually running them. It can be as simple as looking at metadata from a file. It can range from doing disassembly or decompilation of malware code to symbolic execution, which is something like virtual execution of a binary without actually executing it in a real environment. Conversely, dynamic analysis is the process of analyzing a piece of malware when you are running it in a live environment. In this case, you are often looking at the behavior of the malware and looking at the side effects of what it is doing. You are running tools like process monitor and sysmon to see what kinds of artifacts a piece of malware produces after it is run. We also use automated analysis. Oftentimes if you are looking at malware you want to automate things just to speed up the process to save time. However, use caution, as with automated analysis sometimes things get missed because you are trying to do things generically. If a piece of malware contains things like anti-debugging routines or anti-analysis mechanisms, you may want to perform a manual analysis. You need to pick the right tools for the job. DISCLAIMER: Our videos are strictly for documentary, educational, and entertainment purposes only. Imitation or the use of any acts depicted in these videos is solely AT YOUR OWN RISK. We (including YouTube) will not be held liable for any injury to yourself or damage to others resulting from attempting anything shown in any our videos. We do not endorse any specific product and this video is not an attempt to sell you a good or service. These videos are free to watch and if anyone attempts to charge for this video notify us immediately. By viewing or flagging this video you are acknowledging the above.
Views: 666 H4rM0n1cH4cK
Regression: Example 3 || Time Series Analysis || Demand Forecasting || Method of Least Squares
 
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Regression is the most important measure in statistical analysis. Most of the analysis in research is built around correlation and regression. This video though talks of regression being used in time series analysis to make demand or other forecasts. The pdf of the question is available at https://goo.gl/J2JPj7 more practice questions are available at https://goo.gl/XY8u2c Visit us at: http://www.adityaclasses.co.in Visit us at: http://adityaclassesbikaner.tumblr.com Like us on Facebook: http://www.facebook.com/AdityaClassesBikaner Follow us on Twitter: http://www.twitter.com/AdityaClasses Follow us on Instagram: https://www.instagram.com/AdityaClassesBikaner Connect on Linkedin: https://www.linkedin.com/in/AdityaClassesBikaner We're here: https://in.pinterest.com/AdityaClassesBikaner
Views: 33 Aditya Classes
SPSS 20 Forecasting
 
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Here I have shown demonstration of Forecasting using SPSS Version 20
Views: 74582 Musa Md.
Time Series Analysis - An Introduction
 
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Quantitative Techniques in Management: Time Series Analysis - An Introduction; Video by Edupedia World (www.edupediaworld.com). All Rights Reserved. Have a look at the other videos on this topic: https://www.youtube.com/playlist?list=PLJumA3phskPH2vSufmMsrBUHbuoQY3G4R Browse through other subjects in our playlist: https://www.youtube.com/channel/UC6E97LDJTFJgzWU7G3CHILw/playlists?sort=dd&view=1
Views: 11513 Edupedia World
Mod-04 Lec-10 Time Series Analysis - I
 
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Stochastic Hydrology by Prof. P. P. Mujumdar, Department of Civil Engineering, IISc Bangalore For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 19058 nptelhrd
Regression: Example 2 || Time Series Analysis || Demand Forecasting || Method of Least Squares
 
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Regression is the most important measure in statistical analysis. Most of the analysis in research is built around correlation and regression. This video though talks of regression being used in time series analysis to make demand or other forecasts. The pdf of the question is available at https://goo.gl/J2JPj7 more practice questions are available at https://goo.gl/XY8u2c Visit us at: http://www.adityaclasses.co.in Visit us at: http://adityaclassesbikaner.tumblr.com Like us on Facebook: http://www.facebook.com/AdityaClassesBikaner Follow us on Twitter: http://www.twitter.com/AdityaClasses Follow us on Instagram: https://www.instagram.com/AdityaClassesBikaner Connect on Linkedin: https://www.linkedin.com/in/AdityaClassesBikaner We're here: https://in.pinterest.com/AdityaClassesBikaner
Views: 46 Aditya Classes
Time Series ARIMA Models
 
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Time Series ARIMA Models https://sites.google.com/site/econometricsacademy/econometrics-models/time-series-arima-models
Views: 246797 econometricsacademy