Time Series Analysis (Georgia Tech) – 3.1.3 – Multivariate Time Series – Data Examples

Time Series Analysis
PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech
Unit 3: Multivariate Time Series Modelling
Part 1: Multivariate Time Series
Lesson: 3 – Multivariate Time Series – Data Examples
Notes, Code, Data: https://tinyurl.com/Time-Series-Analysis-NotesData
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Seq2seq Product on Time-series Data: Exercising and training and Having by using TensorFlow that in fact Masood Krohy

Masood Krohy at April 9, 2019 event of montrealml.dev

Title: Seq2seq Model on Time-series Data: Training and Serving with TensorFlow

Summary: Seq2seq models are a class of Deep Learning models that have provided state-of-the-art solutions to language problems recently. They also perform very well on numerical, time-series data which is of particular interest in finance and IoT, among others. In this hands-on demo/code walkthrough, we explain the model development and optimization with TensorFlow (its low-level API). We then serve the model with TensorFlow Serving and show how to write a client to communicate with TF Serving over the network and use/plot the received predictions.

Code on GitHub: https://github.com/patternedscience/time-series-tf-serving

Bio: Masood Krohy is a Data Science Platform Architect/Advisor and most recently acted as the Chief Architect of UniAnalytica, an advanced data science platform with wide, out-of-the-box support for time-series and geospatial use cases. He has worked with several corporations in different industries in the past few years to design, implement and productionize Deep Learning and Big Data products. He holds a Ph.D. in computer engineering.

This video is a production of PatternedScience Inc.
Website: https://www.patterned.science
LinkedIn: https://www.linkedin.com/company/patterned-science/
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Final Project Presentation
CS671 Deep Learning – Group 13
IIT Mandi

Final Project Presentation
CS671 Deep Learning – Group 13
IIT Mandi

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In this Python for Data Science Tutorial you will learn about Time series Visualization in python using matplotlib and seaborn in jupyter notebook (Anaconda).

This is the 10th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist „the sexiest job of the 21st century.“ Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We’ll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets.

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** Python Data Science Training : https://www.edureka.co/python **
This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial:

1. Why Time Series?
2. What is Time Series?
3. Components of Time Series
4. When not to use Time Series
5. What is Stationarity?
6. ARIMA Model
7. Demo: Forecast Future

Subscribe to our channel to get video updates. Hit the subscribe button above.

Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm

#timeseries #timeseriespython #machinelearningalgorithms

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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

– – – – – – – – – – – – – – – – – – –

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 sales@edureka.co
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Presentation by Emily Fox, Amazon Professor of Machine Learning, UW at the 2018 GeekWire Cloud Tech Summit: geekwire.com/cloudsummit

Most of the recent success stories in machine learning involve a clear prediction goal combined with a massive (benchmark) training dataset. However, many practical machine learning problems do not fit this mold. In this talk, I am going to focus primarily on learning from time-series data, discuss some open challenges beyond prediction, and present paths forward to handle limited data scenarios and notions of interpretability in deep learning models.
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I am sorry for my poor english. I hope it helps you. when i take the data mining course, i had searched it but i couldnt. So i decided to share this video with you.
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Time Series Forecasting Theory | AR, MA, ARMA, ARIMA | Data Science

In this video you will learn the theory of Time Series Forecasting. You will what is univariate time series analysis, AR, MA, ARMA & ARIMA modelling and how to use these models to do forecast. This will also help you learn ARCH, Garch, ECM Model & Panel data models.

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