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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Supply Group And Privacy Full Clinque Display By using Photo

Are you looking to make your next stock portfolio and risk management PPT model a more engaging one? Then instantly download our readymade portfolio risk management PowerPoint show and lay emphasis on your assets, liabilities and strategic planning. Furthermore, you can utilize our PPT sample file to evaluate your company’s risk tolerance potential as well as to analyze the different ways for effective portfolio asset management. Additionally, exclusive template themes like portfolio management process, risk reward matrix, asset allocation, resource capacity planning etc. makes our PPT sample an exceptional one. But that’s not all; our sample presentation PPT also helps to address other related concepts like investment options, investment opportunities, financial risks, interest rate risk etc. In short, wide applications makes our PowerPoint slide deck a must download for professionals like treasurer, chief financial officer, stock portfolio manager and many more. So what are you waiting for? Downloading our presentation deck is easy and most exciting thing is that you will be ready with your project in no time. Make clouds of doubt disappear with our Stock Portfolio And Risk Management Complete Powerpoint Deck With Slides. They allow your thoughts to come floating in.
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Exchange-traded funds (ETFs) have grown in popularity among investors over the past decades. This video can help you understand the risks and potential rewards of investing in this asset class.

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Foretelling of Coffee The production By using Moment in time Assortment Examination

When facing a business question, it’s important to put thought into the problem, try to understand what data is needed for your analysis, try different techniques to arrive at an answer, and be prepared to fail.

Most analyses don’t lead to a crisp answer the first time. Iteration is key.

Watch this lecture, led by Dan Trepanier, Faculty Director at SCU, as he looks at a beer production dataset to help you:

1. Understand the nature of the data
2. Understand trends, seasonality, and cyclicality of beer consumption and production
3. Come up with a model to predict beer production over a 3 year horizon
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In this webinar, Kris Skrinak, AWS Partner Solution Architect, will deep dive into time series forecasting with deep neural networks using Amazon SageMaker built-in algorithm: DeepAR Forecasting. Learn more at – https://amzn.to/2Q73Vgr.

Learn more – https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html
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Compelling Neural Network Framework By using Stochastic Rewiring

Dynamic Neural Network Structures Through Stochastic Rewiring

Robert Legenstein, Graz University of Technology
https://simons.berkeley.edu/talks/robert-legenstein-4-17-18
Computational Theories of the Brain

Soledad Villar: "Graph neural networks for combinatorial optimization problems"

Machine Learning for Physics and the Physics of Learning 2019
Workshop IV: Using Physical Insights for Machine Learning

„Graph neural networks for combinatorial optimization problems“
Soledad Villar – New York University

Abstract: Graph neural networks are natural objects to express functions on graphs with relevant symmetries. In this talk we introduce graph neural networks, motivated by techniques in statistical physics. We explain how they are being used to learn algorithms for combinatorial optimization problems on graphs from data (like clustering, max-cut and quadratic assignment), in supervised and unsupervised manners. We also show a connection between universal approximation of invariant functions and the graph isomorphism problem.

Institute for Pure and Applied Mathematics, UCLA
November 21, 2019

For more information: http://www.ipam.ucla.edu/mlpws4
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Developing Wise Espresso Uses with the use of Neural Platforms, Using the Neuroph Platform

Artificial neural networks provide solutions for ill-defined problems including recognition, such as image, character, and gesture; forecasting, such as stock market prediction; and optimization, such as JVM parameters. This session introduces the Neuroph Java open source neural network framework and shows how to use it, via demos and code samples. The session is intended for developers interested in artificial intelligence and the problems outlined above. You can learn more at: http://neuroph.sourceforge.net/
You will learn about
• The Java neural network framework Neuroph and its features and development
• Solving problems by using neural networks
• Using neural networks for image recognition, stock market prediction, and JVM tweaking
• Why and how Neuroph moved to the NetBeans platform and the resulting gains

Copyright © 2013 Oracle and/or its affiliates. Oracle® is a registered trademark of Oracle and/or its affiliates. All rights reserved. Oracle disclaims any warranties or representations as to the accuracy or completeness of this recording, demonstration, and/or written materials (the „Materials“). The Materials are provided „as is“ without any warranty of any kind, either express or implied, including without limitation warranties of merchantability, fitness for a particular purpose, and non-infringement.
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Benefit Computing Using Python & Ml

Stock Price Prediction Using Python & Machine Learning (LSTM).
In this video you will learn how to create an artificial neural network called Long Short Term Memory to predict the future price of stock.

NOTE: In the video to calculate the RMSE I put the following statement:
rmse=np.sqrt(np.mean((predictions- y_test)**2))

When in fact I meant to put :
rmse=np.sqrt(np.mean(((predictions- y_test)**2)))

You can use the following statements to calculate RMSE:
1. rmse =np.sqrt(np.mean(((predictions- y_test)**2)))
2. rmse = np.sqrt(np.mean(np.power((np.array(y_test)-np.array(predictions)),2)))
3. rmse = np.sqrt(((predictions – y_test) ** 2).mean())

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►Article :
https://medium.com/@randerson112358/stock-price-prediction-using-python-machine-learning-e82a039ac2bb

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#StockPrediction #Python #MachineLearning
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An Introduction Into Foretelling of Using STATA

A brief introduction into STATA done for my CAT 125 Digital Media Storytelling Project.

Dataset: https://drive.google.com/file/d/1BT2H2XUn1MXDLiSwPP9wIVKey5yQ6b0q/view?usp=sharing

Nonlinear Trend Curves:
Exponential trend (v={a}*exp({b}*t))
Logarithmic (v={a}+{b}*ln(t))
Power curve (v={a}*t^{b})
Reciprocal (v={a}+{b}/t)
Log reciprocal (v={a}*exp({b}/t))
Modified exponential (v={a}+{b}*exp({c}*t))
Gompertz (v={a}*exp({b}*exp({c}*t)))
Logistic (v={a}/(1+{b}*exp({c}*t)))

STATA Resource Page: https://www.stata.com/features/documentation/
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