Views: 54149
Jordan Kern

Many tasks in finance, science and engineering require the ability to control a dynamic system to maximise some objective. Designing controllers based on a physical understanding of systems is often time consuming and inaccurate. One is generally forced to make an increasing amount of simplifying assumptions the more complex the system is. For instance, friction, `stiction' and flex are difficult to model and, thus, frequently ignored. An alternative is automatic learning of control. By training a probabilistic model of a system's dynamics directly from data, we can predict how the system will evolve over time. Such predictive power enables evaluation and comparison of controllers by simulating how they would effect a system. Controller design can thus be seen as the process of finding which controller would optimise a user-supplied objective function in simulation.

Views: 461
Microsoft Research

Views: 1893
SCMKeele

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: 183529
MIT OpenCourseWare

#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: 13931
Prashant Puaar

#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: 7379
Prashant Puaar

1. Example Dataset (FBExample.csv) Download Here:
https://drive.google.com/open?id=1zLdsfBk8T31pEnm61trfb9hFMjmewGb5
2. MA Analysis in Python
https://www.youtube.com/watch?v=TOeXpCHtrxk

Views: 29959
The Data Science Show

A gentle intro to the Moving Average model in Time Series Analysis

Views: 155
ritvikmath

( 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: 87792
edureka!

This video provides a methodology for diagnosing whether a given series is AR(1) or MA(1). Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti

Views: 59302
Ben Lambert

Presenter: Prof Rob J Hyndman
Slides available: http://robjhyndman.com/talks/melbournerug/
Melbourne R Users: http://www.meetup.com/MelbURN-Melbourne-Users-of-R-Network/
Other R User Group Videos: http://www.vcasmo.com/user/drewconway
Info on upcoming book: http://robjhyndman.com/researchtips/fpp/
I will look at the various facilities for time series forecasting available in R, concentrating on the forecast package. This package implements several automatic methods for forecasting time series including foreasts from ARIMA models, ARFIMA models and exponential smoothing models. I will also look more generally at how to go about forecasting non-seasonal data, seasonal data, seasonal data with high frequency, and seasonal data with multiple frequencies. Examples will be taken from my own consulting experience. I will give an overview of what's possible and available and where it is useful, rather than give the mathematical details of any specific time series methods.
Rob J Hyndman is Professor of Statistics at Monash University and Director of the Monash University Business and Economic Forecasting Unit. He completed a science degree at the University of Melbourne in 1988 and a PhD on nonlinear time series modelling at the same university in 1992. He has worked at the University of Melbourne, Colorado State University, the Australian National University and Monash University. Rob is Editor-in-Chief of the "International Journal of Forecasting" and a Director of the International Institute of Forecasters. He has written over 100 research papers in statistical science. In 2007, he received the Moran medal from the Australian Academy of Science for his contributions to statistical research. Rob is co-author of the well-known textbook "Forecasting: methods and applications" (Wiley, 3rd ed., 1998) and of the book "Forecasting with exponential smoothing: the state space approach" (Springer, 2008). He is also the author of the widely-used "forecast" package for R. For over 25 years, Rob has maintained an active consulting practice, assisting hundreds of companies and organizations on forecasting problems. His recent consulting work has involved forecasting electricity demand, tourism demand and the Australian government health budget. More information is available on his website at robjhyndman.com.
Thank you to Pedro Olaya for filming the talk and Deloitte for providing the venue.

Views: 71881
Jeromy Anglim

Views: 76
Florence

3 existing time theories, a personal experience, and a proposal for new theories.

Views: 8212
Kris Fuehr

How to predict time-series data using a Recurrent Neural Network (GRU / LSTM) in TensorFlow and Keras. Demonstrated on weather-data.
https://github.com/Hvass-Labs/TensorFlow-Tutorials

Views: 67442
Hvass Laboratories

THIS IMPORTANT TUTORIAL HAS BEEN PREPARED BASED ON REQUEST OF SOME SUBSCRIBERS.

Views: 22569
DECODE BD

#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: 13617
Prashant Puaar

NonLinear Time Series Analysis in C#.NET using both Math.Net and Cronos for both Linear and Nonlinear(i.e. Mackey-Glass equations) Granger Causality for simulated data, Threshold Autoregressive Models(TAR) and Functional Autogressive Models(FAR) are shown. This is a just a small sample of what can be done. More fully developed models are part of the BMI-1 code base for the specification, estimation and forecasting of nonparametric and parametric nonlinear time series models that can be used as web services with Time Maestro, Model Maker 2 and other applications.

Views: 3414
TheCromwellWorkshop

Time Series Analysis
PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech
Unit 5: Other Time Series Methods
Part 2: Multivariate Time Series Modelling
Lesson: 4 - Functional Data Analysis
Notes, Code, Data: https://tinyurl.com/Time-Series-Analysis-NotesData

Views: 261
Bob Trenwith

Learn to do some basic Time Series Forecasting using Excel.

Views: 3110
ExcelStatistics

These are videos from the Nonlinear Dynamics course offered on Complexity Explorer (complexity explorer.org) taught by Prof. Liz Bradley. These videos provide a broad introduction to the field of nonlinear dynamics, focusing both on the mathematics and the computational tools that are so important in the study of chaotic systems. The course is aimed at students who have had at least one semester of college-level calculus and physics, and who can program in at least one high-level language (C, Java, Matlab, R, ...).
After a quick overview of the field and its history, we review the basic background that students need in order to succeed in this course. We then dig deeper into the dynamics of maps—discrete-time dynamical systems—encountering and unpacking the notions of state space, trajectories, attractors and basins of attraction, stability and instability, bifurcations, and the Feigenbaum number. We then move to the study of flows, where we revisit many of the same notions in the context of continuous-time dynamical systems. Since chaotic systems cannot, by definition, be solved in closed form, we spend some time thinking about how to solve them numerically, and learning what challenges arise in that process. We then learn about techniques and tools for applying all of this theory to real-world data and close with a number of interesting applications: control of chaos, prediction of chaotic systems, chaos in the solar system, and uses of chaos in music and dance.
In each unit of this course, students will begin with paper-and-pencil exercises regarding the corresponding topics, and then write computer programs that operationalize the associated mathematical algorithms. This will not require expert programming skill, but you should be comfortable translating basic mathematical ideas into code. Any computer language that supports simple plotting—points on labelled axes—will suffice for these exercises. We will not ask you to turn in your code, but simply report and analyze the results that your code produces.

Views: 1056
Complexity Explorer

#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 estimate for 20:
Year 1946 1956 1966 1976 1986 1996 2006 2016
Value 3.9 5.3 7.3 9.6 12.9 17.1 23.2 30.9
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: 3544
Prashant Puaar

Lecture Series on Chaos, Fractals and Dynamical Systems by Prof.S.Banerjee,Department of Electrical Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in.

Views: 11878
nptelhrd

Taking a look at seasonal data (Sunspots) and creating a function that can be used to predict values in the future.
(Recorded with http://screencast-o-matic.com)

Views: 4066
Nick Losee

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: 64279
Enthought

Lecture Series on Chaos, Fractals and Dynamical Systems by Prof.S.Banerjee,Department of Electrical Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in.

Views: 7819
nptelhrd

** 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: 83192
edureka!

Devavrat Shah, Massachusetts Institute of Technology
https://simons.berkeley.edu/talks/devavrat-shah-3-29-18
Societal Networks

Views: 1012
Simons Institute

This Time Series Analysis (Part-1) 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: Forecast 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
- Twitter: https://twitter.com/simplilearn
- LinkedIn: https://www.linkedin.com/company/simplilearn/
- Website: https://www.simplilearn.com
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Views: 32631
Simplilearn

Time Series Analysis
PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech
Unit 3: Multivariate Time Series Modelling
Part 1: Multivariate Time Series
Lesson: 2 - Multivariate Time Series - Basic Concepts
Notes, Code, Data: https://tinyurl.com/Time-Series-Analysis-NotesData

Views: 295
Bob Trenwith

See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1
Download a trial: https://goo.gl/PSa78r
A key challenge with the growing volume of measured data in the energy sector is the preparation of the data for analysis. This challenge comes from data being stored in multiple locations, in multiple formats, and with multiple sampling rates. This presentation considers the collection of time-series data sets from multiple sources including Excel files, SQL databases, and data historians. Techniques for preprocessing the data sets are shown, including synchronizing the data sets to a common time reference, assessing data quality, and dealing with bad data. We then show how subsets of the data can be extracted to simplify further analysis.
About the Presenter: Abhaya is an Application Engineer at MathWorks Australia where he applies methods from the fields of mathematical and physical modelling, optimisation, signal processing, statistics and data analysis across a range of industries. Abhaya holds a Ph.D. and a B.E. (Software Engineering) both from the University of Sydney, Australia. In his research he focused on array signal processing for audio and acoustics and he designed, developed and built a dual concentric spherical microphone array for broadband sound field recording and beam forming.

Views: 56938
MATLAB

These are videos from the Nonlinear Dynamics course offered on Complexity Explorer (complexity explorer.org) taught by Prof. Liz Bradley. These videos provide a broad introduction to the field of nonlinear dynamics, focusing both on the mathematics and the computational tools that are so important in the study of chaotic systems. The course is aimed at students who have had at least one semester of college-level calculus and physics, and who can program in at least one high-level language (C, Java, Matlab, R, ...).
After a quick overview of the field and its history, we review the basic background that students need in order to succeed in this course. We then dig deeper into the dynamics of maps—discrete-time dynamical systems—encountering and unpacking the notions of state space, trajectories, attractors and basins of attraction, stability and instability, bifurcations, and the Feigenbaum number. We then move to the study of flows, where we revisit many of the same notions in the context of continuous-time dynamical systems. Since chaotic systems cannot, by definition, be solved in closed form, we spend some time thinking about how to solve them numerically, and learning what challenges arise in that process. We then learn about techniques and tools for applying all of this theory to real-world data and close with a number of interesting applications: control of chaos, prediction of chaotic systems, chaos in the solar system, and uses of chaos in music and dance.
In each unit of this course, students will begin with paper-and-pencil exercises regarding the corresponding topics, and then write computer programs that operationalize the associated mathematical algorithms. This will not require expert programming skill, but you should be comfortable translating basic mathematical ideas into code. Any computer language that supports simple plotting—points on labelled axes—will suffice for these exercises. We will not ask you to turn in your code, but simply report and analyze the results that your code produces.

Views: 366
Complexity Explorer

Simple technical analysis on Jakarta Composite Index (JCI) [Indeks Harga Saham gabungan] 1997 - 2013 using python, pandas, numpy and matplotlib modules.
Installation:
sudo apt-get install python-pandas
sudo apt-get install python-numpy
sudo apt-get install python matplotlib

Views: 20266
kholidfu

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: 14115
Jason Delaney

Practical Time Series Analysis
PLAYLIST: https://tinyurl.com/TimeSeriesPlaylist
3 - Stationarity
4.1 - Difference equations

Views: 495
Bob Trenwith

Dr. Chris Kulp is a Professor of Physics at Lycoming College in Williamsport, PA and an avid researcher in the field of nonlinear dynamics and chaos. He specializes in Nonlinear Time Series Analysis. In this episode, we discuss nonlinear and chaotic systems and how he studies them. We also discuss the growing field of machine learning and its applications in industry and academia. Artificial Intelligence and its implications on the work force are also discussed.
#NonlinearScience #Physics #Chaotic
For more episodes or information about "The State of The Universe with Brendan Drachler" visit thestateoftheuniverse.com or follow Brendan on Twitter and Instagram @BrendanDrachler.
The State of the Universe is an accessible science and social podcast hosted by Astrophysicist Brendan Drachler. Listen to Brendan and other renowned members of the scientific community discuss and explain the cutting edge research occurring across the world today!

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The State of the Universe

In this video, you will learn how to find the demand forecast using linear regression.

Views: 75278
maxus knowledge

Developing Forecast Models from Time Series Data in MATLAB Part 2
Abhaya Parthy, MathWorks
Are you looking to increase your data analysis capabilities? Do you need to perform complex analytics and automate cumbersome repetitive tasks such as batch processing? Do you need to make your programs accessible to others?
During this presentation, we demonstrate how you can use MATLAB to develop nonlinear predictive models from historical time-series measurements. As a working case study, a forecast model of short-term electricity loads for the Australian market using BOM and AEMO data is presented. This case study applies nonlinear tree bagging regression and neural network modelling techniques. At the end of the case study, the MATLAB forecast model is converted into a deployable plug-in for Microsoft Excel.
#Time_Series_Data_in_MATLAB

Views: 114
TO Courses

The brain is an intrinsically nonlinear system, yet the dominant methods used to generate network models of functional connectivity from fMRI data use linear methods. Although these approaches have been used successfully, they are limited in that they can find only linear relations within a system we know to be nonlinear. This study employs a highly specialized genetic programming system which incorporates multiple enhancements to perform symbolic regression, a sophisticated and computationally rigorous type of regression analysis that searches for declarative mathematical expressions to describe relationships in observed data with any provided set of basis functions. Publicly available fMRI data from the Human Connectome Project were segmented into meaningful regions of interest and highly nonlinear mathematical expressions describing functional connectivity were generated. These nonlinear expressions exceed the explanatory power of traditional linear models and allow for more accurate investigation of the underlying physiological connectivities.
Computationally Assisted Mathematical Discovery
and Experimental Mathematics: ACMES 2
12-15 May 2016, London, Ontario, Canada
James Hughes, Department of Computer Science, Western University
May 14, 2016
Visit the Rotman website for more information on applications, events, project descriptions and openings. http://www.rotman.uwo.ca
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Views: 241
Rotman Institute of Philosophy

Take this course for on edX: https://www.edx.org/course/policy-analysis-using-interrupted-time-ubcx-itsx
↓ More info below. ↓
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ABOUT THIS COURSE
Interrupted time series analysis and regression discontinuity designs are two of the most rigorous ways to evaluate policies with routinely collected data. ITSx comprehensively introduces analysts to interrupted time series analysis (ITS) and regression discontinuity designs (RD) from start to finish, including definition of an appropriate research question, selection and setup of data sources, statistical analysis, interpretation and presentation, and identification of potential pitfalls.
At the conclusion of the course, students will have all the tools necessary to propose, conduct and correctly interpret an analysis using ITS and RD approaches. This will help them position themselves as a go-to person within their company, government department, or academic department as the technical expert on this topic.
ITS and RD designs avoid many of the pitfalls associated with other techniques. As a result of their analytic strength, the use of ITS and RD approaches has been rapidly increasing over the past decade. These studies have cut across the social sciences, including:
Studying the effect of traffic speed zones on mortality
Quantifying the impact of incentive payments to workers on productivity
Assessing whether alcohol policies reduce suicide
Measuring the impact of incentive payments to physicians on quality of care
Determining whether the use of HPV vaccination influences adolescent sexual behavior
WHAT YOU'LL LEARN
The strengths and drawbacks of ITS and RD studies
Data requirements, setup, and statistical modelling
Interpretation of results for non-technical audiences
Production of compelling figures

Views: 3827
edX

The third in a five-part series on time series data. In this video, I explain how to use smoothing methods to smooth data series or make forecasts. The methods covered include:
- moving averages
- centered moving average
- weighted moving average
- exponential smoothing

Views: 20151
Jason Delaney

In this video you will learn the theory behind the ARMA process in time series analysis
For Analytics Study Packs visit : http://analyticuniversity.com/

Views: 18739
Analytics University

The second in a five-part series on time series data. In this video, I explain how to evaluate forecasting methods using various measures of forecasting error. The measures covered include:
- mean absolute error (MAE)
- mean square error (MSE)
- mean absolute percentage error (MAPE)

Views: 22366
Jason Delaney

- By: Pere Colet, IFISC
- Date: 2011-03-24 15:00:00
- Description:

In this video you will learn the theory behind moving average models
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Views: 19938
Analytics University