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MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Peter Kempthorne This is the second of three lectures introducing the topic of time series analysis, describing multivariate time series, representation theorems, and least-squares estimation. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 17069 MIT OpenCourseWare

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Views: 9485 Simplilearn

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Views: 14740 Simplilearn

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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: 7912 nptelhrd

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Views: 41470 edureka!

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د.عصام مهدي Dr.Esam Mahdi
Views: 1305 iugaza1

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MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Peter Kempthorne This is the first of three lectures introducing the topic of time series analysis, describing stochastic processes by applying regression and stationarity models. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 168020 MIT OpenCourseWare

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Views: 39062 Jordan Kern

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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: 71798 edureka!

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Time Series Analysis: Worked example using the Seasonal Adjustment Method Part 2 of 4
Views: 65641 Simcha Pollack

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د.عصام مهدي Dr.Esam Mahdi
Views: 1571 iugaza1

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Introduction of Time Series Forecasting | Part 2 | Creating and Smoothing Time Series Link to code: http://learnrprg.blogspot.com/2017/11/introduction-of-time-series-forecasting.html Hi guys… from this video, I am starting time series forecasting video series to take you from beginner to advance user in time series forecasting. This is the second part of time series forecasting video seires and in this video I have covered the ways in which you can create time series in R as well as how you can use the simple moving average and exponential moving average to smooth the time series.Time series

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https://alphabench.com/data/excel-moving-average-tutorial.html Part I of Introductory Time Series Forecasting Series Introduction to Time Series Forecasting with Moving Averages Part II & III can be found at the links below: https://alphabench.com/data/excel-time-series-forcasting.html https://alphabench.com/data/excel-theils-u.html Introduction to time series forecasting using examples of moving average forecasting. We attempt to forecast the price of Gold using the GLD ETF as a proxy for the price of gold. Includes a discussion of commonly used error measures, mean absolute deviation, mean squared error and mean absolute percent error. Error measures are used to determine how good your forecast is, in other words, they measure how far off your forecast is on average.
Views: 5104 Matt Macarty

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Views: 2452 iman

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Mark Pickup, Associate Professor in the Department of Political Science at Simon Fraser University, and Paul Kellstedt, Associate Professor of Political Science at Texas A&M University, describe their ICPSR Summer Program workshop "Time Series Analysis II: Advanced Topics."

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This is the first video about time series analysis. It explains what a time series is, with examples, and introduces the concepts of trend, seasonality and cycles.

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Views: 37949 Siraj Raval

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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: 353931 Analytics University

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Views: 3384 Omnia O H

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Views: 5249 iman

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Time Series Analysis PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech Unit 1: Basic Time Series Analysis Part 1: Basic Time Series Decomposition Lesson: 2 - Time Series Decomposition - Basic Definition and Examples Notes, Code, Data: https://tinyurl.com/Time-Series-Analysis-NotesData
Views: 484 Bob Trenwith

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Speaker: Christopher Batey Time series data is everywhere: IoT, sensor data, financial transactions. The industry has moved to databases like Cassandra to handle the high velocity and high volume of data that is now common place. However data is pointless without being able to process it in near real time or do batch analytics. That's where Spark combined with Cassandra comes in, what was one just your storage system can be transformed into your analytics system, and you'll be surprised how easy it is! Apache Cassandra is an open source distributed database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple datacenters, with asynchronous masterless replication allowing low latency operations for all clients. Cassandra also places a high value on performance. In 2012, University of Toronto researchers studying NoSQL systems concluded that "In terms of scalability, there is a clear winner throughout our experiments. Cassandra achieves the highest throughput for the maximum number of nodes in all experiments" although "this comes at the price of high write and read latencies Apache Spark is a fast and general engine for large-scale data processing. Venue: Wilkins Gustave Tuck Lecture Theatre, UCL ---- video by Meetupvideo (http://www.meetupvideo.com) real-time nosql statistics talks

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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.

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Part 1: http://www.youtube.com/watch?v=gHdYEZA50KE&feature=youtu.be Part 3: http://www.youtube.com/watch?v=kcfiu-f88JQ&feature=youtu.be This is Part 2 of a 3 part "Time Series Forecasting in Excel" video lecture. Be sure to watch Part 1 before watching this part and Part 3 upon completing Part 1 and 2. The links for 1 and 3 are in the video as well as above.

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Code used in this tutorial: https://github.com/codebasics/py/tree/master/pandas/15_ts_date_range Time series analysis is crucial in financial data analysis space. Pandas has in built support of time series functionality that makes analyzing time series extremely efficient. In this tutorial, we will see how date_range function allows to generate datetimeindex with specific start and end dates. It can also generate periods with different frequencies such as hourly,daily,monthly, weekly etc. We will then cover how asfreq function can be used to resample dataframe to a different frequency. Website: http://codebasicshub.com/ Facebook: https://www.facebook.com/codebasicshub Twitter: https://twitter.com/codebasicshub Google +: https://plus.google.com/106698781833798756600
Views: 13871 codebasics

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In this lecture, we discuss What is a time series? Autoregressive Models Moving Average Models Integrated Models ARMA, ARIMA, SARIMA, FARIMA models
Views: 8249 Scholartica Channel

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ACCA F2 Time Series Analysis Free lectures for the ACCA F2 Management Accounting / FIA FMA Exams
Views: 12673 OpenTuition

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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: 16868 The Data Science Show

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د.عصام مهدي Dr.Esam Mahdi
Views: 715 iugaza1

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QUANTITATIVE METHODS TIME SERIES ANALYSIS

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Views: 10447 Spark Summit

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1. Basic intro to R and financial time series manipulation 2. Stationarity and tests for unit root 3. ARIMA and GARCH models 4. Forecasting
Views: 6818 Interactive Brokers

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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: 5809 nptelhrd

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In this lecture, we discuss What is a time series? Autoregressive Models Moving Average Models Integrated Models ARMA, ARIMA, SARIMA, FARIMA models
Views: 79648 Scholartica Channel

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Time Series Analysis: Worked example using the Seasonal Adjustment Method Part 3 of 4
Views: 41686 Simcha Pollack

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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

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This video is suitable for TIME SERIES CA CPT | TIME SERIES CA FOUNDATION | CA FOUNDATION TIME SERIES | TIME SERIES CS FOUNDATION | TIME SERIES ANALYSIS CA | TIME SERIES BCOM 2ND YEAR | TIME SERIES ANALYSIS CS FOUNDATION |TIME SERIES MOVING AVERAGE METHOD | TIME SERIES ANALYSIS CMA | TIME SERIES ANALYSIS | TIME SERIES ANALYSIS EXAMPLES | TIME SERIES ANALYSIS INTRODUCTION | TIME SERIES GRAPHICAL METHOD | METHOD OF SEMI AVERAGE IN TIME SERIES | METHOD OF MOVING AVERAGE IN TIME SERIES | TIME SERIES ANALYSIS DEFINITION | TIME SERIES ANALYSIS FORECASTING | TIME SERIES FORECASTING To watch complete course click here :- https://www.vidyakul.com/super-saver/super-saver-by-chandan-sir For Videos related call at :- 9818434684 For Books related enquiry :- 8010201786 For any other Enquiry :- 9953633448 Mail ID :- [email protected]

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Views: 9671 iman

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Presenter: Nick Clinton Description: This session will cover time series topics including linear modeling, auto-correlation, cross-correlation, auto-regression, smoothing and iteration. Prerequisites: Intermediate to advanced programming with the Earth Engine API. Slides: goo.gl/lMwd2Y Codelab: goo.gl/6Ep5VC

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Time Series Analysis PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech Unit 4: Modelling Heteroskedacity Part 2: GARCH Model Lesson 1 - GARCH Model Notes, Code, Data: https://tinyurl.com/Time-Series-Analysis-NotesData
Views: 225 Bob Trenwith

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We the svcians gives you the simplest method to solve the math's. we try to make it as easy as we can make. needs your support guys. for further doubts and inquiry please whatsApp on 9867475692.

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The first in a five-part series on time series data. In this video, I introduce time series data. I discuss the nature of time series data, visualizing data with a time series plot, identifying patterns in a time series plot and some applications of time series data.
Views: 98901 Jason Delaney

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د. عصام مهدي Dr. Esam Mahdi
Views: 699 iugaza1

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See how to create line graphs of entire time series or for subseries using the -tin()- function. Created using Stata 12. Copyright 2011-2017 StataCorp LLC. All rights reserved.
Views: 71870 StataCorp LLC

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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

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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

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Part 1: http://www.youtube.com/watch?v=gHdYEZA50KE&feature=youtu.be Part 2: http://www.youtube.com/watch?v=5C012eMSeIU&feature=youtu.be This is Part 3 of a 3 part "Time Series Forecasting in Excel" video lecture. Be sure to watch Part 1 and 2 before watching this part. The links for Parts 1 and 2 are in the video as well as above.

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Time Series analysis Bangla Tutorial Part-2 ( Three Yearly Moving Average) Time Series analysis Bangla Tutorial Part-2 (Business Statistics) Business Statistics. Business Forecasting and Time Series Analysis: This video is contributed by Lecturer Md Mostafizur Rahman. Govt. Janata College, Please Like, Comment and Share the Video among your friends and family members For latest updates subscribe My channel .
Views: 1240 Online Education BD

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SPSS training on Conjoint Analysis by Vamsidhar Ambatipudi
Views: 30763 Vamsidhar Ambatipudi

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