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: 62907
vcefurthermaths

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

Views: 101946
Dr. B. R. Ambedkar Govt. College Kaithal

QUANTITATIVE METHODS TIME SERIES ANALYSIS

Views: 202541
Adhir Hurjunlal

#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: 93594
Prashant Puaar

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: 13720
Jason Delaney

#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: 50712
Prashant Puaar

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: 175835
MIT OpenCourseWare

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: 4582
MathsAcademyUK

Time Series Analysis: Introduction to the model;
Seasonal Adjustment Method
Part 1 of 4

Views: 185377
Simcha Pollack

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: 13121
Analytics University

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: 59293
Svtuition

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: 812190
Jalayer Academy

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.

Views: 16726
Dr Nic's Maths and Stats

VCE Further Maths Tutorials. Core (Data Analysis) Tutorial: Patterns and Trends in Time Series Plots. How to tell the difference between seasonal, cyclical and .
Management Studies; Quantitative Techniques: Time Series Analysis | Trend Measurement | Method of Least Square; Video by Edupedia World .
VCE Further Maths Tutorials. Core (Data Analysis) Tutorial: Smoothing Time Series Data. This tute runs through mean and median smoothing, from a table and .

Views: 102
Gail Stehr

A GCSE Statistics help video to go through the main ideas on calculating moving averages for time series data and how to then plot and draw a trend line to then calculate the mean seasonal variation to predict future values.

Views: 51778
davidpye3142

( 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: 78655
edureka!

Obtain Trend by Free Hand Curve, Semi Average, Moving Average Methods in Time Series.

Views: 15353
Statistical Analysis

This Time Series Analysis (Part-1) in R tutorial will help you understand what is time series, why time series, components of time series, when not to use time series, why does a time series have to be stationary, how to make a time series stationary and at the end, you will also see a use case where we will forecast car sales for 5th year using the given data.
Link to Time Series Analysis Part-2: https://www.youtube.com/watch?v=Y5T3ZEMZZKs
You can also go through the slides here: https://goo.gl/RsAEB8
A time series is a sequence of data being recorded at specific time intervals. The past values are analyzed to forecast a future which is time-dependent. Compared to other forecast algorithms, with time series we deal with a single variable which is dependent on time. So, lets deep dive into this video and understand what is time series and how to implement time series using R.
Below topics are explained in this " Time Series in R Tutorial " -
1. Why time series?
2. What is time series?
3. Components of a time series
4. When not to use time series?
5. Why does a time series have to be stationary?
6. How to make a time series stationary?
7. Example: Forcast car sales for the 5th year
To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6
#DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning
Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.
Why learn Data Science with R?
1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT
The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice.
1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.
3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice.
The Data Science with R is recommended for:
1. IT professionals looking for a career switch into data science and analytics
2. Software developers looking for a career switch into data science and analytics
3. Professionals working in data and business analytics
4. Graduates looking to build a career in analytics and data science
5. Anyone with a genuine interest in the data science field
6. Experienced professionals who would like to harness data science in their fields
Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Time-Series-Analysis-gj4L2isnOf8&utm_medium=Tutorials&utm_source=youtube
For more information about Simplilearn courses, visit:
- Facebook: https://www.facebook.com/Simplilearn
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Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Views: 23169
Simplilearn

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: 3338
Edupedia World

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: 24857
Edupedia World

SPSS training on Conjoint Analysis by Vamsidhar Ambatipudi

Views: 34280
Vamsidhar Ambatipudi

(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: 25543
Wild About Statistics

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: 108111
mrmathshoops

Time Series Analysis
PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech
Unit 1: Basic Time Series Analysis
Part 3: Data Example - Emergency Department Volume
Lesson: 2 - Case Study - Trend and Seasonality Estimation
Notes, Code, Data: https://tinyurl.com/Time-Series-Analysis-NotesData

Views: 157
Bob Trenwith

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: 11346
Gopal Malakar

** 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: 62043
edureka!

What is the difference between Autoregressive (AR) and Moving Average (MA) models? Explanation Video: https://www.youtube.com/watch?v=2kmBRH0caBA

Views: 19441
The Data Science Show

We offer the most comprehensive and easy to understand video lectures for CFA and FRM Programs. To know more about our video lecture series, visit us at www.fintreeindia.com
This Video lecture was recorded by Mr. Utkarsh Jain, during his live CFA Level II Classes in Pune (India). This video lecture covers following key area's:
1. The predicted trend value for a time series, modeled as either a linear trend or a log-linear trend, given the estimated trend coefficients.
2. Factors that determine whether a linear or a log-linear trend should be used with a particular time series
3. Limitations of trend models
4. Requirement for a time series to be covariance stationary
5. Significance of a series that is not stationary.
6. Structure of an autoregressive (AR) model of order p
7. One- and two-period-ahead forecasts given the estimated coefficients.
8.How autocorrelations of the residuals can be used to test whether the autoregressive model fits the time series
9.Concept of mean reversion
10. Calculation of a mean-reverting level.
11. In-sample and out-of-sample forecasts
12. The forecasting accuracy of different time-series models based on the root mean squared error criterion
13. Instability of coefficients of time-series models.
14. Characteristics of random walk processes
15. implications of unit roots for time-series analysis
16. When unit roots are likely to occur and How to test for them
17. How a time series with a unit root can be transformed so it can be analyzed with an AR model.
18. Steps of the unit root test for nonstationarity
19. The relation of the test to autoregressive time-series models.
20. How to test and correct for seasonality in a time-series model
21. autoregressive conditional heteroskedasticity (ARCH)
22. how time-series variables should be analyzed for nonstationarity and/or cointegration before use in a linear regression.
23. an appropriate time-series model to analyze a given investment problem, and justify that choice.
24. Practice Questions with Solutions

Views: 14701
FinTree

#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: 6634
Prashant Puaar

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: 5585
iman

ACCA F2 Time Series Analysis
Free lectures for the ACCA F2 Management Accounting / FIA FMA Exams

Views: 13434
OpenTuition

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: 3669
PyData

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: 21696
Svtuition

Straight line trend Least Square method year 2005 solved sums | Statistics | Mathematics | Mathur Sir Classes
#MathurSirClasses #StudyMaterial
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Video Recording and Editing by - Gyankaksh Educational Institute (9051378712)
https://www.youtube.com/channel/UCFzUEzxnRDsbWIA5rnappwQ

Views: 54019
Mathur Sir Classes

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

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dataminingincae

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: 62169
Enthought

Using dummy variables and multiple linear regression to forecast trend and seasonality

Views: 103747
profMattDean

Dokuz Eylul University Faculty of Business Business Administration Department QMT3001 Business Forecasting Class Video Series

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Prof Dr Sabri Erdem

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
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Grooming Education Academy by Chandan Poddar

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.
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SGBAU Commerce

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
Notes, Code, Data: https://tinyurl.com/Time-Series-Analysis-NotesData

Views: 476
Bob Trenwith

Long term trend analysis in statistics

Views: 15375
Vijay Prakash Chaturvedi

Education
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Xtream chanel

#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
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Prashant Puaar

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.
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Analytics University

In this video I show you how to forecast using Time Series Analysis. I use the Additive Method where y = t + s.
The example I use is a Google keyword search on the term 'ice cream'. It is expected that this search term is cyclical, which is perfect for time series analysis. This is due to the seasonal nature of ice cream consumption or on-line search.
Firstly, I calculate the seasonal variation and then the adjusted seasonal average. This is required so that I can use these seasonal average figures to represent the likely seasonal figures for the following year that I'm forecasting.
Secondly, I estimate the trend. Once the trend is estimated, the data for the following year can be forecasted using the above formula.
Thanks for watching and why not check out my previous 'How to' videos on regression and correlation (also used in forecasting).
►Simple Linear Regression Part 1: https://www.youtube.com/watch?v=sXPEgOXA7OA
►Simple Linear Regression Part 2: https://www.youtube.com/watch?v=7zPV-84PzM8
►Simple Linear Regression Part 3: https://www.youtube.com/watch?v=981XPygx9iY
►Simple Linear Regression Part 4: https://www.youtube.com/watch?v=uHWqJ1BrJeA
►How to Calculate the Simple Linear Regression Equation: https://youtu.be/8l7BUma-Jj4
►How to Calculate the Correlation Coefficient https://youtu.be/2u1gX7GplrA
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Ice Cream Toy by Mo Riza (Flickr)

Views: 16158
Frank Conway

Introduction of Time Series Forecasting | Part 4 | Time Series Forecasting for Trend and Seasonal component
Link to code - http://learnrprg.blogspot.com/2017/11/introduction-of-time-series-forecasting_6.html
Hi guys… in this video I have talked about the background logic of exponential smoothing , how you can decompose a time series to clearly know trend component, seasonal component and random component, trend component and seasonal component I have shown a way by which you do the time series forecasting and predicting future values using simple exponential smoothing process.

Views: 2685
Data Science Tutorials

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
Notes, Code, Data: https://tinyurl.com/Time-Series-Analysis-NotesData

Views: 386
Bob Trenwith