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Working with Time Series Data in MATLAB
 
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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: 50929 MATLAB
Time Series Analysis and Forecast - Tutorial 7 - TSAF New Feature
 
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To download the TSAF GUI, please click here: http://www.mathworks.com/matlabcentral/fileexchange/54276-time-series-analysis-and-forecast Please check out www.sphackswithiman.com for more tutorials.
Views: 5076 iman
Time Series Analysis Basic by Using Matlab (Trial & Error) Part 1
 
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Time Series Analysis Basic by Using Matlab (Trial & Error)
Views: 762 Phayung Meesad
Spectral Analysis with MATLAB
 
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See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r MathWorks engineers illustrate techniques of visualizing and analyzing signals across various applications. Using MATLAB and Signal Processing Toolbox functions we show how you can easily perform common signal processing tasks such as data analysis, frequency domain analysis, spectral analysis and time-frequency analysis techniques. This webinar is geared towards scientists / engineers who are not experts in signal processing. Webinar highlights include: A practical introduction to frequency domain analysis. How to use spectral analysis techniques to gain insight into data. Ways to easily carry out signal measurement tasks. View example code from this webinar here. About the Presenter Kirthi Devleker is the product marketing manager for Signal Processing Toolbox at MathWorks. He holds a MSEE degree from San Jose State University
Views: 34863 MATLAB
Time Series Analysis and Forecast - Tutorial  4 - TSAF (Example 1)
 
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To download the TSAF GUI, please click here: http://www.mathworks.com/matlabcentral/fileexchange/54276-time-series-analysis-and-forecast Please check out www.sphackswithiman.com for more tutorials.
Views: 5201 iman
Time Series Analysis and Forecast - Tutorial  1 - Concept
 
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To download the TSAF GUI, please click here: http://www.mathworks.com/matlabcentral/fileexchange/54276-time-series-analysis-and-forecast Please check out www.sphackswithiman.com for more tutorials.
Views: 10352 iman
MATLAB Applications - (NAR) Time Series Neural Networks
 
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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: 3411 Nick Losee
How to export data from Simulink to Matlab and how to work with Time Series Structure
 
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This video demo shows how to extract data from Simulink into Matlab for offline analysis.
Views: 23918 Kody Powell
Let's play around with Time Series Analysis Using Matlab (Trial & Error) 1
 
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Let's play around with Time Series Using Matlab (Trial & Error)
Views: 47 Phayung Meesad
News Sentiment Analysis Using MATLAB and RavenPack
 
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Use MATLAB® to analyze news sentiment with data from RavenPack®, including retrieving historical data and real-time data. Also, create trading rules based on news sentiment score. To Request a trial of Datafeed Toolbox, visit: https://www.mathworks.com/programs/trials/trial_request.html?prodcode=DF&s_iid=main_trial_DF_tb&s_eid=PEP_12669 Datafeed Toolbox™ provides access to current, intraday, historical, and real-time market data from leading financial data providers. By integrating these data feeds into MATLAB®, you can perform analyses, develop models, and create visualizations that reflect current financial and market behaviors. The toolbox also provides functions to export MATLAB data to some data service providers. You can establish connections from MATLAB to retrieve historical data or subscribe to real-time streams from data service providers. With a single function call, the toolbox lets you customize queries to access all or selected fields from multiple securities over a specified time period. You can also retrieve intraday tick data for specified intervals and store it as time-series data. Supported data providers include Bloomberg®, FactSet®, FRED®, Haver Analytics®, Interactive Data™, IQFEED®, Kx Systems®, SIX Financial Information, Thomson Reuters®, and Yahoo!® Finance.
Views: 2311 MATLAB
Time Series Analysis Basic by Using Matlab (Trial & Error)
 
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Time Series Analysis Basic by Using Matlab (Trial & Error)
Views: 201 Phayung Meesad
Time Series Analysis Basic by Using Matlab (Trial & Error) Part 2
 
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Time Series Analysis Basic by Using Matlab (Trial & Error)
Views: 89 Phayung Meesad
Time Series Analysis - 2.1.6 - Autocorrelation Function ACF
 
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Practical Time Series Analysis PLAYLIST: https://tinyurl.com/TimeSeriesPlaylist 2 - Visualizing and Modelling Time Series 1.6 - Autocorrelation Function ACF
Views: 3617 Bob Trenwith
Time Series Analysis and Forecast - Tutorial 3 - ARMA
 
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To download the TSAF GUI, please click here: http://www.mathworks.com/matlabcentral/fileexchange/54276-time-series-analysis-and-forecast Please check out www.sphackswithiman.com for more tutorials.
Views: 6796 iman
Developing Forecast Models from Time Series Data in MATLAB Part 1   Video   MATLAB
 
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Developing Forecast Models from Time-Series Data in MATLAB - Part 1 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: 42 TO Courses
Trying Recurrent Neural Network for Time Series Analysis Using Matlab (Trial & Error)
 
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Trying Recurrent Neural Network for Time Series Analysis Using Matlab (Trial & Error)
Views: 3339 Phayung Meesad
Signal Analysis Made Easy
 
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Download a trial: https://goo.gl/PSa78r See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 In this webinar, we will showcase how easy it is to perform Signal Analysis tasks in MATLAB. The presentation is geared towards users who want to analyze signal data regardless of their signal processing expertise. You will learn common signal analysis techniques such as visualizing and pre-processing the signal, filtering, identifying and measuring relevant features. We will use signals from variety of application areas and demonstrate how to : Import and visualize signal data Pre-process and filter signals to enhance the quality of the signal Visualize the signal in time domain and frequency domains Analyze and measure trends, peaks, and other characteristic features of the signal Create a MATLAB app to package the analysis into a single file and distribute to others
Views: 63384 MATLAB
Stock Market prediction system |+91-8146105825 for query  | Machine Learning
 
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Hello friends today I’m going to show you how the stock market prediction system works and how machine learning helps you to get the exact estimation of the stock market. For any further help contact us at [email protected] visit us at http://www.researchinfinitesolutions.com/ Direct at :: +91-6239359461 Whatsapp at :: +91-6239359461
Views: 34579 Fly High with AI
Data Forecasting Using Time SerIes Neural Network| Neural Networks Topic | MATLAB Helper ®
 
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Learn about the application of Time Series Neural Network using a simple data forecasting example in MATLAB script.Study Neural Network with MATLABHelper course. For more such amazing content, visit MATLABHelper.com. Enroll today in one of our course at https://mlhp.link/courses Leave a review for us on Facebook: https://mlhp.link/FacebookReviews Like us on Facebook: https://mlhp.link/facebook Join our FB Community: https://mlhp.link/FBgroup Tweet to us: https://mlhp.link/twitter Join us on Linkedin: https://mlhp.link/linkedin Join us on Google+: https://mlhp.link/googleplus Follow us on Instagram: https://mlhp.link/instagram Share your feedback with us at [email protected]
Views: 571 MATLAB Helper ®
Technical Analysis- using Financial Time Series in MATLAB
 
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http://www.qcfinance.in/ To do technical analysis using Matlabs and using the financial time series toolbox
Views: 2690 Satyadhar Joshi
Trying Recurrent Neural Network for Time Series Analysis Using Matlab (Trial & Error)
 
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Trying Recurrent Neural Network for Time Series Analysis Using Matlab (Trial & Error)
Views: 4456 Phayung Meesad
Maths Tutorial: Smoothing Time Series Data (statistics)
 
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VCE Further Maths Tutorials. Core (Data Analysis) Tutorial: Smoothing Time Series Data. This tute runs through mean and median smoothing, from a table and straight onto a graph, using 3 and 5 mean & median smoothing and 4 point smoothing with centring. For more tutorials, visit www.vcefurthermaths.com
Views: 56207 vcefurthermaths
Morlet wavelets in time and in frequency
 
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Convolution requires two time series: The data and the kernel. The data is what you already have (EEG/MEG/LFP/etc); here you will learn about the most awesomest kernel for time-frequency decomposition of neural time series data: The Morlet wavelet. This video uses the following MATLAB code: http://mikexcohen.com/lecturelets/morlet/morletWavelet.m For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/
Views: 8344 Mike X Cohen
Time Series Neural Network GUI| Neural Networks Topic | MATLAB Helper ®
 
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Learn how to use the Graphic User Interface (GUI) for Time Series Neural Network in MATLAB.Study Neural Network with MATLABHelper course. For more such amazing content, visit MATLABHelper.com. Enrol today in one of our course at https://mlhp.link/courses Leave a review for us on Facebook: https://mlhp.link/FacebookReviews Like us on Facebook: https://mlhp.link/facebook Join our FB Community: https://mlhp.link/FBgroup Tweet to us: https://mlhp.link/twitter Join us on Linkedin: https://mlhp.link/linkedin Join us on Google+: https://mlhp.link/googleplus Follow us on Instagram: https://mlhp.link/instagram Share your feedback with us at [email protected]
Views: 307 MATLAB Helper ®
Two Effective Algorithms for Time Series Forecasting
 
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In this talk, Danny Yuan explains intuitively fast Fourier transformation and recurrent neural network. He explores how the concepts play critical roles in time series forecasting. Learn what the tools are, the key concepts associated with them, and why they are useful in time series forecasting. Danny Yuan is a software engineer in Uber. He’s currently working on streaming systems for Uber’s marketplace platform. This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl For more awesome presentations on innovator and early adopter topics, check InfoQ’s selection of talks from conferences worldwide http://bit.ly/2tm9loz Join a community of over 250 K senior developers by signing up for InfoQ’s weekly Newsletter: https://bit.ly/2wwKVzu
Views: 35672 InfoQ
Data Analysis: Detrending data series to avoid false correlations
 
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Spreadsheets like Excel and Google Sheets are powerful tools that quickly calculate correlations between data sets that can allow you to make causative inferences. Here I show you how to detrend data to ensure that your correlations are real and not due to some other factor that impacts the data. SUPER SAVER. Take advantage of this limited time offer. Use coupon code YTQ12016 valid until March 31th 2016 to enroll in my forecasting course for the low, low price of $5 (normally $45). http://www.udemy.com/business-forecasting-with-google-sheets/
Views: 19062 Spreadsheet Sage
Time Series analysis
 
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Watch this brief (10 minutes or so!!) video tutorial on how to do all the calculations required for a Time Series analysis of data on Microsoft Excel. Try and do your best to put up with the pommie accent. The data for this video can be accessed at https://sites.google.com/a/obhs.school.nz/level-3-statistics-and-modelling/time-series
Views: 108123 mrmathshoops
Time Series Analysis and Forecast - Tutorial 5 - TSAF (Example 2)
 
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To download the TSAF GUI, please click here: http://www.mathworks.com/matlabcentral/fileexchange/54276-time-series-analysis-and-forecast Please check out www.sphackswithiman.com for more tutorials.
Views: 2633 iman
Developing Forecast Models from Time Series Data in MATLAB Part 2
 
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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: 22 TO Courses
Creating ARIMA Models Using Econometric Modeler App
 
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This quick introduction will show you how to use Econometric Modeler App for time-series analysis, including data transformation, visualization, statistical tests, and model fitting. The featured example is based on airline passengers’ data, which is shipped together with Econometric Toolbox™. Learn more about Econometric Toolbox: https://goo.gl/pYH9xJ Get a Trial of Econometric Toolbox: https://goo.gl/3dRTtV You will learn how to: • Visualize data using interactive plots, including ACF and PACF plots • Transform data using log transformation • Perform hypothesis testing using augmented Dickey-Fuller test • Create seasonal ARIMA models for time-series analysis • Perform statistical analyses on residuals
Views: 1404 MATLAB
Time Series Analysis and Forecast - Tutorial 6 - TSAF (Example 3)
 
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To download the TSAF GUI, please click here: http://www.mathworks.com/matlabcentral/fileexchange/54276-time-series-analysis-and-forecast Please check out www.sphackswithiman.com for more tutorials.
Views: 2055 iman
Maglev Modeling with Neural Time Series App
 
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Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Model the position of a levitated magnet as current passes through an electromagnet beneath it. For more videos, visit http://www.mathworks.com/products/neural-network/examples.html
Views: 7024 MATLAB
Import Data and Analyze with MATLAB
 
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Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 377789 APMonitor.com
Plotting Frequency Spectrum using Matlab
 
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Outlines the key points to understanding the matlab code which demonstrates various ways of visualising the frequency content of a signal at http://dadorran.wordpress.com/2014/02/17/plot_freq_spectrum/. This code is published in a more visually friendly way at http://dadorran.wordpress.com/2014/02/20/plotting-frequency-spectrum-using-matlab/
Views: 190094 David Dorran
Time Series Analysis and Forecast - Tutorial 2 - Trend and Seasonality
 
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To download the TSAF GUI, please click here: http://www.mathworks.com/matlabcentral/fileexchange/54276-time-series-analysis-and-forecast Please check out www.sphackswithiman.com for more tutorials.
Views: 5586 iman
Signal Processing and Machine Learning Techniques for Sensor Data Analytics
 
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Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe ------------------------------------------------------------------------- An increasing number of applications require the joint use of signal processing and machine learning techniques on time series and sensor data. MATLAB can accelerate the development of data analytics and sensor processing systems by providing a full range of modelling and design capabilities within a single environment. In this webinar we present an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone. We introduce common signal processing methods in MATLAB (including digital filtering and frequency-domain analysis) that help extract descripting features from raw waveforms, and we show how parallel computing can accelerate the processing of large datasets. We then discuss how to explore and test different classification algorithms (such as decision trees, support vector machines, or neural networks) both programmatically and interactively. Finally, we demonstrate the use of automatic C/C++ code generation from MATLAB to deploy a streaming classification algorithm for embedded sensor analytics.
Views: 15692 MATLAB
Time Series Forecasting with LSTM Deep Learning
 
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A quick tutorial on Time Series Forecasting with Long Short Term Memory Network (LSTM), Deep Learning Techniques. The detailed Jupyter Notebook is available at https://anaconda.org/jaganadhg/eneryconsumeforecast_deeplearning/notebook
Views: 20527 Jaganadh Gopinadhan
Data Predictor Using Neural Networks
 
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In this project , I built a program using neural networks in MATLAB for predicting the pollution in a lake near chemical plant in Saudi Arabia. I received the daily measured pollution for the last one & half year , Accordingly , I imported the daily measured data into MATLAB & converted the date using MATLAB to numbers . Second step was plotting the measured data in 2-D graph , I used Time-Series toolbox to plot the daily measurements against the pollution . Third step was making a neural network(NN) program to learn & adapt itself based on the pollution history in addition to its ability to be updated & trained again as per the new entered data while the mean squared error (mse) for the predicted data shall be less than %5 . Fourth & last step was using the already trained NN to predict the future pollution & based on the predicted data , we can judge how to operate the chemical plant. NOTE : Please play the video in HD mode . You can Purshase this code by following this link https://www.robotics-world-fze.com/on-line-soft-store/data-predictor-using-neural-networks-windows-32-bit-only Regards Saqer Khalil +966-540591074 Jeddah , Saudi Arabia [email protected]
Views: 119229 Saqer Khalil
Regression and ARIMA Models in MATLAB (Chinese)
 
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Cool regression and time-series models in newer version of MATLAB
Views: 3239 Fen YNWA
The Periodogram for Power Spectrum Estimation
 
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http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Introduces the periodogram approach to estimating the power spectrum of a time series, including characterization of the bias and variance of the periodogram.
Views: 26285 Barry Van Veen
Broad overview of EEG data analysis analysis
 
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This lecture is a very broad introduction to the most commonly used data analyses in cognitive electrophysiology. There is no math, no Matlab, and no data to download. For more information about MATLAB programming: https://www.udemy.com/matlab-programming-mxc/?couponCode=MXC-MATLAB10 For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/
Views: 12937 Mike X Cohen
Getting Started with Neural Network Toolbox
 
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Use graphical tools to apply neural networks to data fitting, pattern recognition, clustering, and time series problems. - Get a Free MATLAB Trial: https://goo.gl/C2Y9A5 - Ready to Buy: https://goo.gl/vsIeA5
Views: 295872 MATLAB
How DTW (Dynamic Time Warping) algorithm works
 
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In this video we describe the DTW algorithm, which is used to measure the distance between two time series. It was originally proposed in 1978 by Sakoe and Chiba for speech recognition, and it has been used up to today for time series analysis. DTW is one of the most used measure of the similarity between two time series, and computes the optimal global alignment between two time series, exploiting temporal distortions between them. Source code of graphs available at https://github.com/tkorting/youtube/blob/master/how-dtw-works.m The presentation was created using as references the following scientific papers: 1. Sakoe, H., Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustic Speech and Signal Processing, v26, pp. 43-49. 2. Souza, C.F.S., Pantoja, C.E.P, Souza, F.C.M. Verificação de assinaturas offline utilizando Dynamic Time Warping. Proceedings of IX Brazilian Congress on Neural Networks, v1, pp. 25-28. 2009. 3. Mueen, A., Keogh. E. Extracting Optimal Performance from Dynamic Time Warping. available at: http://www.cs.unm.edu/~mueen/DTW.pdf
Views: 36277 Thales Sehn Körting
Data Analysis with MATLAB for Excel Users
 
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This webinar highlights how MATLAB can work with Excel. Get a Free MATLAB Trial: https://goo.gl/C2Y9A5 Ready to Buy: https://goo.gl/vsIeA5 Learn MATLAB for Free: https://goo.gl/xIiHyG Many technical professionals find that they run into limitations using Excel for their data analysis applications. This webinar highlights how MATLAB can supplement the capabilities of Excel by providing access to thousands of pre-built engineering and advanced analysis functions and versatile visualization tools. Learn more about using MATLAB with Excel: http://goo.gl/3vkFMW Learn more about MATLAB: http://goo.gl/YKadxi Through product demonstrations you will see how to: • Access data from spreadsheets • Plot data and customize figures • Perform statistical analysis and fitting • Automatically generate reports to document your analysis • Freely distribute your MATLAB functions as Excel add-ins This webinar will show new features from the latest versions of MATLAB including new data types to store and manage data commonly found in spreadsheets. Previous knowledge of MATLAB is not required. About the Presenter: Adam Filion holds a BS and MS in Aerospace Engineering from Virginia Tech. His research involved nonlinear controls of spacecraft and periodic orbits in the three-body problem. After graduating he joined the MathWorks Engineering Development Group in 2010 and moved to Applications Engineering in 2012.
Views: 237245 MATLAB
EEG data and indexing in Matlab
 
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This video describes how to identify time/frequency/electrode points in your data, as well as a few tips for Matlab programming and debugging. The video uses the following files (and also the topoplot function, which is free to download with the eeglab toolbox): http://mikexcohen.com/lecturelets/indexing/indexing.m http://mikexcohen.com/lecturelets/sampleEEGdata.mat A full-length course on MATLAB programming and debugging can be found here: https://www.udemy.com/matlab-programming-mxc/?couponCode=MXC-MATLAB10 For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/
Views: 6398 Mike X Cohen
Understanding Wavelets, Part 1: What Are Wavelets
 
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This introductory video covers what wavelets are and how you can use them to explore your data in MATLAB®. •Try Wavelet Toolbox: https://goo.gl/m0ms9d •Ready to Buy: https://goo.gl/sMfoDr The video focuses on two important wavelet transform concepts: scaling and shifting. The concepts can be applied to 2D data such as images. Video Transcript: Hello, everyone. In this introductory session, I will cover some basic wavelet concepts. I will be primarily using a 1-D example, but the same concepts can be applied to images, as well. First, let's review what a wavelet is. Real world data or signals frequently exhibit slowly changing trends or oscillations punctuated with transients. On the other hand, images have smooth regions interrupted by edges or abrupt changes in contrast. These abrupt changes are often the most interesting parts of the data, both perceptually and in terms of the information they provide. The Fourier transform is a powerful tool for data analysis. However, it does not represent abrupt changes efficiently. The reason for this is that the Fourier transform represents data as sum of sine waves, which are not localized in time or space. These sine waves oscillate forever. Therefore, to accurately analyze signals and images that have abrupt changes, we need to use a new class of functions that are well localized in time and frequency: This brings us to the topic of Wavelets. A wavelet is a rapidly decaying, wave-like oscillation that has zero mean. Unlike sinusoids, which extend to infinity, a wavelet exists for a finite duration. Wavelets come in different sizes and shapes. Here are some of the well-known ones. The availability of a wide range of wavelets is a key strength of wavelet analysis. To choose the right wavelet, you'll need to consider the application you'll use it for. We will discuss this in more detail in a subsequent session. For now, let's focus on two important wavelet transform concepts: scaling and shifting. Let' start with scaling. Say you have a signal PSI(t). Scaling refers to the process of stretching or shrinking the signal in time, which can be expressed using this equation [on screen]. S is the scaling factor, which is a positive value and corresponds to how much a signal is scaled in time. The scale factor is inversely proportional to frequency. For example, scaling a sine wave by 2 results in reducing its original frequency by half or by an octave. For a wavelet, there is a reciprocal relationship between scale and frequency with a constant of proportionality. This constant of proportionality is called the "center frequency" of the wavelet. This is because, unlike the sinewave, the wavelet has a band pass characteristic in the frequency domain. Mathematically, the equivalent frequency is defined using this equation [on screen], where Cf is center frequency of the wavelet, s is the wavelet scale, and delta t is the sampling interval. Therefore when you scale a wavelet by a factor of 2, it results in reducing the equivalent frequency by an octave. For instance, here is how a sym4 wavelet with center frequency 0.71 Hz corresponds to a sine wave of same frequency. A larger scale factor results in a stretched wavelet, which corresponds to a lower frequency. A smaller scale factor results in a shrunken wavelet, which corresponds to a high frequency. A stretched wavelet helps in capturing the slowly varying changes in a signal while a compressed wavelet helps in capturing abrupt changes. You can construct different scales that inversely correspond the equivalent frequencies, as mentioned earlier. Next, we'll discuss shifting. Shifting a wavelet simply means delaying or advancing the onset of the wavelet along the length of the signal. A shifted wavelet represented using this notation [on screen] means that the wavelet is shifted and centered at k. We need to shift the wavelet to align with the feature we are looking for in a signal.The two major transforms in wavelet analysis are Continuous and Discrete Wavelet Transforms. These transforms differ based on how the wavelets are scaled and shifted. More on this in the next session. But for now, you've got the basic concepts behind wavelets.
Views: 175213 MATLAB
Basic Analysis: Standardizing time series data with indexes, ratios, and trends
 
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How to videos for community planners and economic developers
Views: 1448 Dave Swenson
Total Harmonic Distortion MATLAB Simulink, FFT Analysis in MATLAB Simulink
 
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In this video, Total Harmonic Distortion in MATLAB Simulink, FFT Analysis in MATLAB Simulink or THD analysis in matlab Simulink powergui shown. total harmonic distortion in matlab simulink Music: https://www.bensound.com
Views: 16185 Techno Trip