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Maths Tutorial: Patterns and Trends in Time Series Plots (statistics)
 
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VCE Further Maths Tutorials. Core (Data Analysis) Tutorial: Patterns and Trends in Time Series Plots. How to tell the difference between seasonal, cyclical and random variation patterns, as well as positive and negative secular trends. For more tutorials, visit www.vcefurthermaths.com
Views: 58552 vcefurthermaths
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 THE BEST EXAMPLE
 
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QUANTITATIVE METHODS TIME SERIES ANALYSIS
Views: 194924 Adhir Hurjunlal
Time Series - Trend and Variation
 
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How to identify the type of trend and variation from a graph. Upward linear trend. Upward non-linear trend. Downward linear trend. Downward non-linear trend. No trend. Seasonal variation. Random variation. Short-term variation.
Views: 4386 MathsAcademyUK
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: 87152 Prashant Puaar
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: 183747 Simcha Pollack
Time Series - 4 Method of Least Squares - Fitting of Linear Trend - Even years
 
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#Statistics #Time #Series #Business #Forecasting #Linear #Trend #Values #LeastSquares #Fitting #Even #Period 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 Even No. of Years If a n is even, the transformation is x = YEAR - (arithmetic mean of two middle years) / Half Interval NOTE It is not compulsory to divide the numerator by "Half Interval". There are two types of authors, suggesting for such kind of change of scale and not suggesting. I have discussed this point of change of scale in some of lectures because in India and other countries of Indian subcontinent and Asia, in many reference books, and in the books published by the boards of examinations, the authors have suggested this kind of change of scale. Case Fit a straight line equation and obtain trend value: Year 2009 2010 2011 2012 2013 2014 2015 2016 Y (value) 80 90 92 83 94 99 92 104 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: 46808 Prashant Puaar
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: 41270 edureka!
Trend Analysis
 
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This video is the part of financial statement analysis lectures part 6. In this video, you will learn to make trend analysis with past figures of sales.
Views: 55458 Svtuition
Time series: Seasonal, Trend, Random & Noise components in Time series data - ExcelR
 
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ExcelR : Learn about the various components of the forecasting data series including Seasonal, Trend & Random components. Things you will Learn in this Video 1)Trend Components 2)Sesonal Component 3)Irregular Component 4)Timeseries Component 5)Quarterly Sales Example To buy Elearning course on DataScience click here https://goo.gl/oMiQMw To enroll for the virtual online course click here https://goo.gl/m4MYd8 To register for classroom training click here https://goo.gl/UyU2ve SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For Time-series Forecasting Strategy click Here https://goo.gl/zpHR2f For Generating Forecasting Time Series click Here https://goo.gl/ZSAVh8 For Types of Forecasting Timeseries click here https://goo.gl/Aq3Fhr #ExcelRSolutions #SesionalComponents#TrendComponents #datascience #datasciencetutorial #datascienceforbeginners #datasciencecourse ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
Time series - practice problem 18.32 - trend estimation and seasonal dummies
 
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A walkthrough of a forecasting practice problem explaining how to: - develop a time series plot - use regression methods to estimate trend - use dummy variables to estimate seasonal influences - forecast with and without seasonal influences
Views: 11416 Jason Delaney
Gretl Tutorial 7: Comparing Time Series Trend Models
 
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We run linear, quadratic, and exponential trend models using a time series dataset and compare their performance based on the Root Mean Squared Error (RMSE) of their predictions. TABLE OF CONTENTS: 00:00 Introduction 00:11 What we will do in this Video 00:44 Plot Time Series in Gretl 02:23 Run and Visually Examine Linear Trend Model 04:26 Run and Visually Examine Quadratic Trend 06:28 Run and Visually Examine Exponential Trend 07:26 Discussion on Comparing Models with R-squared 08:56 Discussion on Comparing Models with RMSE 09:22 Store Predictions of each Model 12:01 Compare Descriptive Stats of Predictions 12:41 Converting ln(retail) to retail 13:28 Compare Descriptive Stats of Predictions 13:50 Exporting Predictions to Excel 14:37 Computing RMSE in Excel 17:54 Compare RMSEs 18:30 Comparing Models in Gretl
Views: 10618 dataminingincae
Time series - practice problem 18.30 - trend estimation and seasonal dummies
 
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A walkthrough of a forecasting practice problem explaining how to: - develop a time series plot - use regression methods to estimate trend - use dummy variables to estimate seasonal influences - forecast with and without seasonal influences
Views: 12250 Jason Delaney
Time Series Analysis (Georgia Tech) - 1.1.3 - Decomposition - Trend Estimation
 
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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: 3 - Decomposition - Trend Estimation
Views: 311 Bob Trenwith
Components of Time Series
 
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This is Lecture series on Time Series Analysis Chapter of Statistics. In this part, you will learn the components of time series. Watch all statistics videos at http://svtuition.com/
Views: 20508 Svtuition
Time Series - Forecasting using a Regression Line
 
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Trend line drawn using a regression line and y=a+bx
Views: 4856 MathsAcademyUK
Seasonal Decomposition and Forecasting, Part I
 
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(Index: https://www.stat.auckland.ac.nz/~wild/wildaboutstatistics/ ) How big is the seasonal effect? We’ll discuss two models for decomposing a basic time series plot by separating out the trend, seasonal effect and residuals. After you’ve watched this video, you should be able to answer these questions •What is the basic idea behind an additive model (or additive seasonal decomposition)? •Why do we want to find stable structures in our time series?
Views: 23702 Wild About Statistics
Introducing Time Series Analysis and forecasting
 
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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.
Time Series Analysis | Trend Measurement | Method of Least Square | Measurement of Secular Trend
 
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Management Studies; Quantitative Techniques: Time Series Analysis | Trend Measurement | Method of Least Square; 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: 24553 Edupedia World
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: 783295 Jalayer Academy
Time Series - 4 - Trend Estimation
 
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The fourth in a five-part series on time series data. In this video, I explain how to use an additive decomposition model to: - use regression methods to estimate trend - use dummy variables to estimate seasonal influences - forecast with and without seasonal influences
Views: 13389 Jason Delaney
Time Series Analysis | Measurement of Secular Trend | Methods of Trend Measurement | Secular Trend
 
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Management Studies; Quantitative Techniques in Management: Time Series Analysis | Measurement of Secular Trend | Methods of Trend Measurement | Secular Trend: 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: 3179 Edupedia World
Time series analysis method of least square
 
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Education Please subscribe to our chanel https://www.youtube.com/channel/UCzM-9fwP3g8Vite41TqI-ew
Views: 2838 Xtream chanel
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: 71700 edureka!
Time Series Analysis and Forecast - Tutorial 2 - Trend and Seasonality
 
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To download the TSAF GUI, please click here: http://www.mathworks.com/matlabcentral/fileexchange/54276-time-series-analysis-and-forecast Please check out www.sphackswithiman.com for more tutorials.
Views: 5243 iman
Bugra Akyildiz: Trend Estimation in Time Series Signals
 
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PyData Seattle 2015 Trend estimation is a family of methods to be able to detect and predict tendencies and regularities in time series signals without knowing any information a priori about the signal. Trend estimation is not only useful for trends but also could yield seasonality(cycles) of data as well. I will introduce various ways to detect trends in time series signals. With more and more sensors readily available and collection of data becomes more ubiquitous and enables machine to machine communication(a.k.a internet of things), time series signals play more and more important role in both data collection process and also naturally in the data analysis. Data aggregation from different sources and from many people make time-series analysis crucially important in these settings. Detecting trends and patterns in time-series signals enable people to respond these changes and take actions intelligibly. Historically, trend estimation has been useful in macroeconomics, financial time series analysis, revenue management and many more fields to reveal underlying trends from the time series signals. Trend estimation is a family of methods to be able to detect and predict tendencies and regularities in time series signals without knowing any information a priori about the signal. Trend estimation is not only useful for trends but also could yield seasonality(cycles) of data as well. Robust estimation of increasing and decreasing trends not only infer useful information from the signal but also prepares us to take actions accordingly and more intelligibly where the time of response and to action is important. In this talk, I will introduce following trend estimation methods and compare them in real-world datasets comparing their advantages and disadvantages of each algorithm: - Moving average filtering - Exponential smoothing, - Median filtering, - Bandpass filtering, - Hodrick Prescott Filter, - Gradient Boosting Regressor, - l_1 trend filtering(my own library) Materials Available Slides: http://bugra.github.io/pages/deck/2015-07-25/#/ Github Repo: https://github.com/bugra/pydata-seattle-2015 Notebook Link: https://github.com/bugra/pydata-seattle-2015/blob/master/notebooks/Trend%20Estimation%20Methods.ipynb
Views: 3496 PyData
Time Series Analysis with Python Intermediate | SciPy 2016 Tutorial | Aileen Nielsen
 
03:03:25
Tutorial materials for the Time Series Analysis tutorial including notebooks may be found here: https://github.com/AileenNielsen/TimeSeriesAnalysisWithPython See the complete SciPy 2016 Conference talk & tutorial playlist here: https://www.youtube.com/playlist?list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6.
Views: 59256 Enthought
Time series Analysis (Trend Seasonality (TCSI) Modelling) and forecasting  using Excel
 
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1. Basic Multiplicative Model (TCSI) 2. What are different components like Trend component, cyclic component, seasonal component etc? 3. How to calculate different component in a given series using excel 4. How to forecast using TCSI model (step by step in excel) VSP Group, my partner program. Get connected! https://youpartnerwsp.com/en/join?62916
Views: 11125 Gopal Malakar
Time Series Analysis (Georgia Tech) - 1.1.4 - Trend Estimation - Data Example
 
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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: 4 - Trend Estimation - Data Example
Views: 261 Bob Trenwith
Practical Python Data Science Techniques : Time Series Analysis with Pandas | 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/2eFdLW7]. This video discusses how to analyze time series data using Pandas, observing seasonality and understanding the general trend of a series. o Understand seasonality and trend of a series o Decompose a time series with an additive model o Implement time series analysis with Pandas and statsmodels For the latest Application development video tutorials, please visit http://bit.ly/1VACBzh Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 1279 Packt Video
Time Series - 6 Non-linear Trend - Second Degree Parabola - Quadratic Method
 
16:30
#Statistics #Time #Series #Business #Forecasting #NonLinear #Trend #Values #SecondDegree #Parabola #Quadratic Definitions “A time series may be defined as a sequence of values of same variable corresponding to successive points in time.” – W. Z. Hersch “A time series may be defined as a sequence of repeated measurement of a variable made periodically through time.” – Cecil H. Mayers Analysis of Time Series “The main object of analyzing time series is to understand, interpret and evaluate changes in economic phenomena in the hope of more correctly anticipating the course of future events.” – Hersch A time series is a dynamic distribution, which reveals a good deal of variations over time. Statistical methods are, therefore, required to analyze various types of movements in a time series. There may be cyclical variations in general business activity and there may be short duration seasonal variations. There are also some accidental and random variables. The primary purpose of the analysis of time series is to discover and measure all such types of variations, which characterize a time series. Time series analysis means analyzing the historical patterns of the variable that have occurred in past as a means of predicting the future value of the variable. It helps to identify and explain the following: (i) Any regular or systematic variation in the series of data which is due to seasonality- the ‘seasonal’ (ii) Cyclical patterns. (iii) Trends in the data. (iv) Growth rates of these trends. This method can be useful when no major environmental changes are expected and it does highlight seasonal variations in sales and consumer demand. However, time series analysis is limited when organizations face volatile environments. Components of Time series – The time series are classified into four basic types of variations which are analyzed below: T = Trend S = Seasonal variations C = Cyclic variations I = Irregular fluctuations. This composite series is symbolized by the following general terms: O = T x S x C x I Where O = Original data T = Trend S = Seasonal variations C = Cyclic variations I = Irregular components. This Multiplicative model is to be used when S, C, and I are given in percentages. If, however, their true (absolute) values are known the model takes the additive form i.e., O=T+C+S+I. Algebraic Method For Finding Trend (Method of curve fitting by the principle of Least Squares) Fitting of Non-linear Trend Second Degree parabola / Quadratic Method Standard Equation y = a + bx + cx^2 The following three normal equations are used for estimating 'a', 'b' and 'C'. Normal Equations (i) Σy = na + bΣx + cΣx^2 (ii) Σxy = nΣx + bΣx2 + cΣx^3 (iii) Σx^2y = aΣx^2 + bΣx^3 + cΣx^4 Case Fit a parabola y = a + bx + cx^2 to this data. Estimate the price of the commodity for the year 2017 Year 2011 2012 2013 2014 2015 2016 Price 100 107 128 140 181 299 Time Series, Non-linear Trend, Second Degree Parabola, Method of Least Squares, Statistics, MBA, MCA, BE, CA, CS, CWA, CMA, CPA, CFA, BBA, BCom, MCom, BTech, MTech, CAIIB, FIII, Graduation, Post Graduation, BSc, MSc, BA, MA, Diploma, Production, Finance, Management, Commerce, Engineering , Grade-11, Grade- 12 - www.prashantpuaar.com
Views: 11857 Prashant Puaar
TIME SERIES ANALYSIS
 
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Obtain Trend by Free Hand Curve, Semi Average, Moving Average Methods in Time Series.
Views: 13179 Statistical Analysis
Time Series Analysis in SPSS
 
44:59
SPSS training on Conjoint Analysis by Vamsidhar Ambatipudi
Views: 30723 Vamsidhar Ambatipudi
Time Series - 7 Non-linear Trend - Exponential Trend
 
18:37
#Statistics #Time #Series #Business #Forecasting #NonLinear #Trend #Values #Exponential #Curve #Fitting Exponential Curve Fitting / Exponential Trend Method: Standard Equation: y = AB^x Log y = Log A + x Log B Normal Equations: (i) ∑Log y = n [Log A] + ∑x[Log B] (ii) ∑x Log y = ∑x [Log A] + ∑x^2 [Log B] If we take Y = Log y a = Log A b = Log B, the normal equations will be like - \ ΣY = na + bΣx ΣxY = aΣx + bΣx^2 Case Fit Exponential Curve from the following data. Also find trend values and estimate for 2017: Year 2012 2013 2014 2015 2016 Price 1.6 4.5 13.8 40.2 125 Time Series, Non-linear Trend, Exponential Trend, Method of Least Squares, Statistics, MBA, MCA, BE, CA, CS, CWA, CMA, CPA, CFA, BBA, BCom, MCom, BTech, MTech, CAIIB, FIII, Graduation,Post Graduation, BSc, MSc, BA, MA, Diploma, Production, Finance, Management, Commerce, Engineering , Grade-11, Grade- 12 - www.prashantpuaar.com
Views: 6062 Prashant Puaar
Semi average method in time series analysis
 
07:52
Long term trend analysis in statistics
Moving Average Method of Time Series Analysis - M.com - Statistical Analysis | SGBAU Commerce
 
05:04
In this Video , we have discussed about the Moving Average Method of Time Series Analysis, problem is given and the method to solve that problem is also explained. If u have any doubt, feel free to ask us your query. Check another Videos Issue of Share Complete Journal Entries : http://bit.ly/2JASNW9 Issue of Share at Par : http://bit.ly/2JwTigJ Issue of Share at Discount : http://bit.ly/2kYTSsG Issue of Share at Premium : http://bit.ly/2JDHNr4 Hit Like Button if you loved this Video. And Subscribe to the Channel for More Updates !! Like Our Facebook Page And stay Connected with us : http://bit.ly/2GjV1aC
Views: 17294 SGBAU Commerce
24. How to do trend analysis in SPSS?
 
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Md Aktaruzzaman Assistant Professor, IUT, Gazipur, Bangladesh PhD Student, Monash Uni, Melbourne, Australia
Views: 38926 akhtariut
Detrending of Time Series using R
 
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I have explained how to remove trend from a series which has deterministic trend.
Views: 564 Miklesh Yadav
Time Series Analysis (Georgia Tech) - 1.2.1 - Decomposition - Seasonality Estimation
 
08:27
Time Series Analysis PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech Unit 1: Basic Time Series Analysis Part 2: Trend, Seasonality and Stationarity Lesson: 1 - Decomposition - Seasonality Estimation
Views: 221 Bob Trenwith
Unit Root, Stochastic Trend, Random Walk, Dicky-Fuller test in Time Series
 
22:41
In this video you will learn about Unit roots and how you would detect them in Time Series data. Random stochastic trend is the reason why many time series data exhibit unit root. This is found when the time series data is random walk Stationarity & Non Stationary series Deterministic & Stochastic trend Random Walk Unit root test Dicky-Fuller test for unit root ANalytics Study Pack : 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: 8754 Analytics University
Trend Analysis in a Time Series with Minitab
 
03:44
Dokuz Eylul University Faculty of Business Business Administration Department QMT3001 Business Forecasting Class Video Series
Views: 262 Prof Dr Sabri Erdem
Time Series - 5 Method Least Squares - Non-linear Trend - Second Degree Parabola
 
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#Statistics #Time #Series #Business #Forecasting #NonLinear #Trend #Values #LeastSquares #SecondDegree #Parabola Definitions “A time series may be defined as a sequence of values of same variable corresponding to successive points in time.” – W. Z. Hersch “A time series may be defined as a sequence of repeated measurement of a variable made periodically through time.” – Cecil H. Mayers Analysis of Time Series “The main object of analyzing time series is to understand, interpret and evaluate changes in economic phenomena in the hope of more correctly anticipating the course of future events.” – Hersch A time series is a dynamic distribution, which reveals a good deal of variations over time. Statistical methods are, therefore, required to analyze various types of movements in a time series. There may be cyclical variations in general business activity and there may be short duration seasonal variations. There are also some accidental and random variables. The primary purpose of the analysis of time series is to discover and measure all such types of variations, which characterize a time series. Time series analysis means analyzing the historical patterns of the variable that have occurred in past as a means of predicting the future value of the variable. It helps to identify and explain the following: (i) Any regular or systematic variation in the series of data which is due to seasonality- the ‘seasonal’ (ii) Cyclical patterns. (iii) Trends in the data. (iv) Growth rates of these trends. This method can be useful when no major environmental changes are expected and it does highlight seasonal variations in sales and consumer demand. However, time series analysis is limited when organizations face volatile environments. Components of Time series – The time series are classified into four basic types of variations which are analyzed below: T = Trend S = Seasonal variations C = Cyclic variations I = Irregular fluctuations. This composite series is symbolized by the following general terms: O = T x S x C x I Where O = Original data T = Trend S = Seasonal variations C = Cyclic variations I = Irregular components. This Multiplicative model is to be used when S, C, and I are given in percentages. If, however, their true (absolute) values are known the model takes the additive form i.e., O=T+C+S+I. Algebraic Method For Finding Trend (Method of curve fitting by the principle of Least Squares) Fitting of Non-linear Trend Second Degree parabola / Quadratic Method Standard Equation y = a + bx + cx^2 The following three normal equations are used for estimating 'a', 'b' and 'C'. Normal Equations (i) Σy = na + bΣx + cΣx^2 (ii) Σxy = nΣx + bΣx2 + cΣx^3 (iii) Σx^2y = aΣx^2 + bΣx^3 + cΣx^4 Case Fit a parabolic curve of second degree to the following data: Year 2010 2011 2012 2013 2014 2015 2016 Value 10 12 18 15 13 16 14 Time Series, Non-linear Trend, Second Degree Parabola, Method of Least Squares, Statistics, MBA, MCA, BE, CA, CS, CWA, CMA, CPA, CFA, BBA, BCom, MCom, BTech, MTech, CAIIB, FIII, Graduation, Post Graduation, BSc, MSc, BA, MA, Diploma, Production, Finance, Management, Commerce, Engineering , Grade-11, Grade- 12 - www.prashantpuaar.com
Views: 12026 Prashant Puaar
Time series - practice problem 18.54-55 - deseasonalizing and trend estimation
 
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A walkthrough of a forecasting practice problem explaining how to: - deseasonalize a data series - estimate trend - forecast trend - seasonalize the forecast
Views: 27991 Jason Delaney
CIMA P1 Time series analysis
 
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CIMA P1 Time series analysis Free lectures for the CIMA P1 Exams Management Accounting
Views: 3496 OpenTuition
Straight line trend Least Square method year 2005 solved sums | Statistics | Mathur Sir Classes
 
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Straight line trend Least Square method year 2005 solved sums | 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: 40318 Mathur Sir Classes
Ratio to Trend Method
 
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This is the method of calculating seasonal variation. In ratio to trend method, we will calculate annual trend values. Then on this basis, we will calculate quarterly trend value. Now, it will be easy for us to calculate the ratio of original value to trend value which will be the seasonal indices.
Views: 29919 Svtuition
Time Series analysis
 
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Watch this brief (10 minutes or so!!) video tutorial on how to do all the calculations required for a Time Series analysis of data on Microsoft Excel. Try and do your best to put up with the pommie accent. The data for this video can be accessed at https://sites.google.com/a/obhs.school.nz/level-3-statistics-and-modelling/time-series
Views: 106084 mrmathshoops

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