181 through Multivariate moment in time series forecasting taking advantage of LSTM

For a dataset just search online for ‚yahoo finance GE‘ or any other stock of your interest. Then select history and download csv for the dates you are interested.

Code generated in the video can be downloaded from here:
https://github.com/bnsreenu/python_for_microscopists

Succeed at Numbers Digging through Time frame Series In preparation

In this video, Billy Decker of StatSlice Systems shows you how to start data mining in 5 minutes with the Microsoft Excel data mining add-in*. In this example, we will create a forecasting model that will predict the trend of bikes sales in different regions.

For the example, we will be using a tutorial spreadsheet that can be found on Codeplex at:
https://dataminingaddins.codeplex.com/releases/view/87029

*This tutorial assumes that you have already installed the data mining add-in for Excel and configured the add-in to be pointed at an instance of SQL Server to which you have access rights.
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PyCon HK 2017 that in fact Recurring Neural Platforms in Python: Keras and TensorFlow on time Series Research

PyCon HK 2017 - Recurrent Neural Networks in Python: Keras and TensorFlow for Time Series Analysis

PyCon Hong Kong 2017 Workshop

Recurrent Neural Networks in Python: Keras and TensorFlow for Time Series Analysis – by Matt O’Connor

A look at neural networks, specifically recurrent neural networks, and how to implement them in Python for various applications including time series (stock prediction) analysis, using popular machine learning libraries Keras and TensorFlow

http://pycon.hk/2017/topics/recurrent-neural-networks-in-python-keras-and-tensorFlow-for-time-series-analysis/
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Machine Learning in Time Series Forecasting | Kaggle M5 Forecasting Competition, Part 1: Overview

Kaggle has released a new competition for 0,000 total prize money. In this series we will work together on this competition from start to finish, learning some of the practical tools of machine learning and data analysis with Python.

In part 1 of this series I will go over the objectives and give you an overview of the competition. Please subscribe to stay tuned for more videos in the future.

You will find the competition here:
https://www.kaggle.com/c/m5-forecasting-accuracy
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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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High-Performance Time Series Forecasting

Time series is changing. The demands are greater. Companies now demand scalable & automated forecasting systems that can handle forecasting 10,000+ time series daily or weekly. This is challenging data scientists to adapt to meet the demand. Learn these skills and become invaluable to your organization, accelerating your career in the process. Here’s the playbook for accelerating your career.

PLAYBOOK
=====

✅ Agenda 1:48

✅ The BIG CHANGE 3:34

✅ The 3 PROPERTIES of High-Performance Forecasting Systems 9:40

✅ COMPETITION RESEARCH – What Forecasting Technologies get RESULTS 13:05

✅ 5 COMPETITION TAKEAWAYS 21:44

✅ MACHINE LEARNING RESEARCH – Modeltime 24:34

✅ WHAT ABOUT FEATURE ENGINEERING? – Timetk 30:35

✅ WHAT ABOUT DEEP LEARNING? – GluonTS 34:17

✅ WHAT ABOUT SCALABILITY? – Future 36:39

✅ FORECASTING CHEAT SHEET – This cheatsheet is so valuable 38:22

✅ NEW TIME SERIES COURSE – 43:35

✅ [SPECIAL OFFER] 56:27

COURSES
====

1. 5-Course R-Track Program: https://university.business-science.io/p/5-course-bundle-machine-learning-web-apps-time-series

2. High-Performance Time Series Forecasting (DS4B 203-R): https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting/

#timeseries #forecast

Authors:
Zheyi Pan (Shanghai Jiao Tong University);Yuxuan Liang (National University of Singapore);Weifeng Wang (Shanghai Jiao Tong University);Yong Yu (Shanghai Jiao Tong University);Yu Zheng (JD);Junbo Zhang (JD)

More on https://www.kdd.org/kdd2019/
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Financial Time Series Analysis using Wavelets

1. QX Data Science Event | 10.05.2019 | QX Manor in Frankfurt am Main

Description:

Presentation by Markus Vogl at the 1. QX Data Science Event about Financial Time Series Analysis using Wavelets.
Contains Explanations of Financial Time Series Properties (e.g. Markov, Martingale, Stationarity & Gaussianity versus Fractality & Momentum), Signal Theory (e.g. Fourier Analysis, Short Time Fourier Analysis & Continuous as well as Discrete Wavelet Transformations).
Concludes with outlook into research on Wavelet Neural Networks, Fractals & Chaos Theory.

Partners, Event-Team & Presentor:

University of Applied Sciences Aschaffenburg: https://www.th-ab.de/controlling

Mandelbrot Asset Management GmbH: http://mandelbrot.de/

QX: http://www.quarterly-crossing.de/

Markus Vogl {Business & Data Science} : https://www.vogl-datascience.de
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