Search results “Trend time series analysis”

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

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: 96065
Dr. B. R. Ambedkar Govt. College Kaithal

QUANTITATIVE METHODS TIME SERIES ANALYSIS

Views: 194924
Adhir Hurjunlal

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

#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

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

Views: 183747
Simcha Pollack

#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

** 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
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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
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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).
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Views: 41270
edureka!

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

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

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

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
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

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

Trend line drawn using a regression line and y=a+bx

Views: 4856
MathsAcademyUK

(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

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: 16100
Dr Nic's Maths and Stats

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

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

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

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

Education
Please subscribe to our chanel
https://www.youtube.com/channel/UCzM-9fwP3g8Vite41TqI-ew

Views: 2838
Xtream chanel

( 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!

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

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

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

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
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

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
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Find us on Facebook -- http://www.facebook.com/Packtvideo
Follow us on Twitter - http://www.twitter.com/packtvideo

Views: 1279
Packt Video

#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

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

Views: 13179
Statistical Analysis

SPSS training on Conjoint Analysis by Vamsidhar Ambatipudi

Views: 30723
Vamsidhar Ambatipudi

#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

Views: 6561
SebastianWaiEcon

Long term trend analysis in statistics

Views: 13237
Vijay Prakash Chaturvedi

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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Views: 17294
SGBAU Commerce

Md Aktaruzzaman
Assistant Professor, IUT, Gazipur, Bangladesh
PhD Student, Monash Uni, Melbourne, Australia

Views: 38926
akhtariut

I have explained how to remove trend from a series which has deterministic trend.

Views: 564
Miklesh Yadav

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

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
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Views: 8754
Analytics University

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

Views: 262
Prof Dr Sabri Erdem

#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
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Prashant Puaar

A walkthrough of a forecasting practice problem explaining how to:
- deseasonalize a data series
- estimate trend
- forecast trend
- seasonalize the forecast

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Jason Delaney

CIMA P1 Time series analysis
Free lectures for the CIMA P1 Exams Management Accounting

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OpenTuition

Straight line trend Least Square method year 2005 solved sums | Statistics | Mathematics | Mathur Sir Classes
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Mathur Sir Classes

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.

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Svtuition

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Hashtag 4You

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

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Pool Accessary Options. Types of Pool Water Sanitation. There are a variety of pool water treatment options beyond the traditional chlorine, although it remains the most popular option. Chlorine is added to a pool to combat algae or other bacteria that can gather in the water. Chlorinated water relies on a proper pH balance to prevent an overly chemical-smelling pool. While saline pools, also known as saltwater pools, are not chlorine-free, they consist of a salt-chlorine generator that produces lower levels of chlorine. Mineral water pools are chlorine-free and use disinfecting minerals to prevent bacteria and algae. Pool Maintenance.