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01:24:24
1. Basic intro to R and financial time series manipulation 2. Stationarity and tests for unit root 3. ARIMA and GARCH models 4. Forecasting
Views: 8059 Interactive Brokers

01:05:30
Learn Financial Programming and Timeseries Analysis Basics in R and R Studio Not enough for you? Want to learn more R? Our friends over at DataCamp will whip you into shape real quick if you need help: https://www.datacamp.com/courses/free-introduction-to-r?tap_a=5644-dce66f&tap_s=84932-063f71 Or if you're more of a Python guy, we have an intro to finance for Python course live on DataCamp right now: https://www.datacamp.com/courses/introduction-to-portfolio-analysis-in-r?tap_a=5644-dce66f&tap_s=84932-063f71 Join the Quants by taking our Quant Course at http://quantcourse.com 1) Basics of R Programming / Downloading R 2) Using Data Frames 3) An Intro to the Quantmod Package 4) Reading in Financial Data from Quantmod 5) Using Vectors in R 6) Reading and Writing Data as CSV Files 7) Plotting Timeseries Data in R 8) Working with Split / Dividend Adjusted Data 9) Calculating Log Returns 10) Converting Log Returns to Arithmetic and Vice-Versa 11) Apply Function in R / Working With Multivariate Data 12) Intro to the Performance Analytics Package 13) XTS and Zoo Objects for Financial Data 14) Chart the Cumulative Return of an Asset 15) Chart the Drawdown and Daily Returns of an Asset 16) Charting Multiple Assets at Once in R 17) Merging Different Datasets With Different Indexes 18) Calculating Sharpe Ratios and other Performance Metrics
Views: 27793 QuantCourse

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This tutorial illustrates how to use an ARIMA model to forecast the future values of a stock price. Find more data science and machine learning content at: http://www.michael-grogan.com/
Views: 24313 Michael Grogan

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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: 183424 MIT OpenCourseWare

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Views: 32524 Simplilearn

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Autochartist CEO, Ilan Azbel explains how R can be used in real-time market analysis to build automated trading systems - recorded at a live presentation a the Austin R meetup group, May 27th 2015.
Views: 63764 Autochartist

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Real-time Scenarios - Stock Prediction Application Data Science & Machine Learning Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Get the Code here Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN, C5.0 Decision Tree, Random Forest, Naive Bayes, Apriori
Views: 27278 BharatiDWConsultancy

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In this video, we learn to make predictions using ARIMA model for a basic time series data in R Studio. The data used for this analysis is AirPassengers data set found in the base installation of R.
Views: 1685 Rajesh Dorbala

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Views: 21666 Simplilearn

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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2vFJWfb]. An introduction to What is Forecasting. • Understand the concept of Forecast and its importance • Define in-sample, out-of-sample analysis • Illustrate the use of fitted() and forecast() functions in R to perform multi-step ahead forecast For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 575 Packt Video

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What is the difference between Autoregressive (AR) and Moving Average (MA) models? Explanation Video: https://www.youtube.com/watch?v=2kmBRH0caBA
Views: 23133 The Data Science Show

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Hi guys.. in this part 6 of time series forecasting video series I have taken a real life example of rain fall in india and predicted the future years rains with by producing the arima model and then using the forecast package, I predicted the next few years rain fall values. R arima,arima r,arima in r,arima time series forecasting in r,arima example in R,r arima example ,r arima tutorial,r tutorial for arima,arima tutorial in R,testing time series forecasting model,how to test time series forecasting model,validation technique for time series forecasting model,r time series,time series r,introduction of time series forecasting in r,time series tutorial for beginners,arima real life example in R
Views: 11731 Data Science Tutorials

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Welcome to this quantitative finance series in R! In this tutorial, we'll go over installing necessary dependencies to start and playing a little with the Quantmod package. R is a statistical programming language that's widely used for quantitative finance within hedge funds, investment banks, and other areas as well. R is free and offers a robust set of libraries to analyze all sorts of financial data. ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Resources: ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ -R Download: https://cran.r-project.org/mirrors.html -RStudio Download: https://www.rstudio.com/products/rstudio/download/ ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Links! ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐱‍💻 *Source code to this series + other resources* https://github.com/fdupuis659/Quant-Finance-with-R 👾 *My GitHub Page*: https://github.com/fdupuis659 \ 🐱‍🐉*Add me on LinkedIn*: https://www.linkedin.com/in/francis-dupuis-b4a45a13a/ ❤ *Donate On My Website*: http://programmingforfinance.com/
Views: 2153 codebliss

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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. For training, consulting or help Contact : [email protected] For Study Packs : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 405095 Analytics University

01:26:04
PyData LA 2018 Forecasting time-series data has applications in many fields, including finance, health, etc. There are potential pitfalls when applying classic statistical and machine learning methods to time-series problems. This talk will give folks the basic toolbox to analyze time-series data and perform forecasting using statistical and machine learning models, as well as interpret and convey the outputs. Slides - https://www.slideshare.net/PyData/applying-statistical-modeling-and-machine-learning-to-perform-timeseries-forecasting --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 12518 PyData

01:33:21
PyData London 2015 The tutorial covers standard Python and Open Source tools (pandas, matplotlib, seaborn, R/ggplot, etc.) and recent innovations (TsTables, bcolz, blaze, plot.ly) for financial time series analysis and visualization. In addition, approaches are illustrated for high performance I/O of high frequency financial data. It briefly sheds light on the visualization of real-time/streaming financial data. Slides available here: https://github.com/yhilpisch/pydlon15
Views: 3755 PyData

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In this video we start building a financial time series model, using S&P500 daily OHLCV data, and the windowing, sliding validation, and forecasting performance operator. This Part 1.
Views: 24464 NeuralMarketTrends

01:39:41
Jeffrey Yau: Applied Time Series Econometrics in Python and R PyData San Francisco 2016 Time series data is ubitious, and time series statistical models should be included in any data scientists’ toolkit. This tutorial covers the mathematical formulation, statistical foundation, and practical considerations of one of the most important classes of time series models: the AutoRegression Integrated Moving Average with Explanatory Variables model and its seasonal counterpart. Time series data is ubitious, both within and out of the field of data science: weekly initial unemployment claim, tick level stock prices, weekly company sales, the daily number of steps taken recorded by a wearable, just to name a few. Some of the most important and commonly used data science techniques to analyze time series data are those in developed in the field of statistics. For this reason, time series statistical models should be included in any data scientists’ toolkit. This 120-minute tutorial covers the mathematical formulation, statistical foundation, and practical considerations of one of the most important classes of time series models, AutoRegression Integrated Moving Average with Explanatory Variables (ARIMAX) models, and its Seasonal counterpart (SARIMAX). www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 38917 PyData

01:08:35
د.عصام مهدي Dr.Esam Mahdi
Views: 2075 iugaza1

01:44:35
Topic: Interpretable forecasting of financial time series with deep learning Abstract: In this talk I will present our deep learning approach to forecasting financial multivariate time series which indicate the market sentiment towards a financial asset. The interpretable deep neural network reveals the essential dependence between the time series’ variables, and in contrast to the widely used vector autoregressive model, the deep learning model dynamically adapts the dependence coefficients to the ever-changing market conditions. Thus, the proposed method permits the study of the inter-variable relationships which yields a better understanding of the asset’s future price movements and consequently increases the profitability of the asset’s trading activities. I will conclude the talk with dependence analysis and forecasting performance for financial assets from different sectors and with vastly different market capitalisation. Speaker: Ilija is a machine learning researcher building holistic models of unstructured data from multiple modalities. His diverse, six-year experience as a machine learning researcher includes projects on combining satellite images and census data for complex city models, utilizing movie metadata and watch statistics for recommender systems, and fusing image and text data representations for visual question answering. Currently Ilija is working on developing a unified model of financial data coming from multiple sources applied to portfolio optimization.
Views: 520 YiDu AI

23:36
Time Series in R, Session 1, part 1 (Ryan Womack, Rutgers University) http://libguides.rutgers.edu/data twitter: @ryandata Fixed the script and provided new locations for downloads at https://ryanwomack.com/TimeSeries.R https://ryanwomack.com/data/UNRATE.csv https://ryanwomack.com/data/CPIAUCSL.csv
Views: 115588 librarianwomack

26:48
For more check: shishirshakya.blogspot.com
Views: 35900 Shishir Shakya

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How to fetch economic and financial time series data from public sources https://cran.r-project.org/web/packages/pdfetch/index.html
Views: 6523 Sudin K

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PyData New York City 2017 Time series data is ubiquitous, and time series modeling techniques are data scientists’ essential tools. This presentation compares Vector Autoregressive (VAR) model, which is one of the most important class of multivariate time series statistical models, and neural network-based techniques, which has received a lot of attention in the data science community in the past few years.
Views: 35432 PyData

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Views: 216308 Simplilearn

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The major goal of presentation is to illustrate some of the more important applications of the wavelet analysis to financial data set. The focus is set on identification and description of hidden patterns.
Views: 4107 Data Science Society

13:57
Using quantmod package in R to retrieve Financial Time Series data from Yahoo and Google Finance
Views: 1486 Chuc Nguyen Van

11:51
An example of using Facebook's recently released open source package prophet including, - data scraped from Tom Brady's Wikipedia page - getting Wikipedia trend data - time series plot - handling missing data and log transform - forecasting with Facebook's prophet - prediction - plot of actual versus forecast data - breaking and plotting forecast into trend, weekly seasonality & yearly seasonality components prophet procedure is an additive regression model with following components: - a piecewise linear or logistic growth curve trend - a yearly seasonal component modeled using Fourier series - a weekly seasonal component forecasting is an important tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 23209 Bharatendra Rai

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Forecasting is the process of making predictions of the future based on past and present data and analysis of trends. Forecasting Certification Training - Agenda of this Forecasting Time Series video is to explain why forecasting, Forecasting strategy, EDA & Graphical Representation, Forecasting Components, Forecasting Models & Errors For more visit : https://www.excelr.com/forecasting/ ExcelR Forecasting Certification Training Introduction video : https://youtu.be/X1cc2jgAbTw About Forecasting Certification Training Early knowledge is the wealth, even if that knowledge is bit imperfect!!! Wouldn’t you want to unlock the mystery of predicting the stock market? And many of us want to understand how companies are managing their inventory and other resources by forecasting their sales. Here is the solution in the form forecasting technique also called as time series analysis. Forecasting techniques will be applied for time series data. Forecasting Analytics is considered as one of the major branches in big data analytics. Managers often have to take decisions in uncertain environment and often find themselves in a bad situation due to lack of skills on applying the right analytical techniques on the data. Forecasting techniques helps companies save millions of dollars by adjusting their production schedules and other plans. Forecasting techniques on univariate and multivariate time series analysis have huge applications across the industries and areas such as Operations management, Finance & Risk management, Retails, Telecom and manufacturing. Moving Averages and smoothing methods, Box- Jenkins (ARIMA) methodology, Regression with time series data, Holts-Winter, Arch-Garch and Neural Network are the methods widely used for forecasting. Arch-Garch and Neural Networks are the advanced techniques in the forecasting analytics which will be used to model the high frequency data such as stock market and big data. Electricity usage pattern over a period of years in a region Sales of a product over several years Stock market data Things You Will Learn… Introduction to Forecasting Forecasting Errors Forecasting Methods Smoothing Methods Modeling different components Detecting Anomalies For Full Course Content Visit : https://www.excelr.com/forecasting/ Forecasting steps involves: Data manipulation and cleaning • Problem formulation and data collection • Model building and evaluation • Model implementation to generate forecast • Forecast evaluation Tools You Will Learn… MS-Excel R – Revolution Analytics is recently acquired by Microsoft but still remains to be an open source software ---------------------------------------------------------------------------------- Mode of Trainings : E-Learning Online Training Class Room Training --------------------------------------------------------------------------- For More Info Contact :: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com

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Views: 63013 Siraj Raval

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Views: 12709 DataCamp

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Sequence classification tasks can be solved in a number of ways, including both traditional ML and deep learning methods. Catch Lauren Tran’s talk at the Women in Machine Learning and Data Science meetup as she discusses the general LSTM, CNN, and SVM algorithms, how they work, and how they are applied in sequence labeling tasks with time series data. She'll walk through a practical application of applying these algorithms and techniques to financial transaction data to detect signs of financial distress and predict insolvency.
Views: 4689 Microsoft Developer

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Views: 82856 edureka!

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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: 47699 InfoQ

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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: 67207 Hvass Laboratories

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In this video we continue building a financial time series model, using S&P500 daily OHLCV data, and the windowing, sliding validation, and forecasting performance operator. We test the model with some out of sample S&P500 data.
Views: 16325 NeuralMarketTrends

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Training on Understanding Basic Time Series Data in R by Vamsidhar Ambatipudi
Views: 3553 Vamsidhar Ambatipudi

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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: 56846 MATLAB

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A project on IT Elective III We are teaching how to use Time series analysis
Views: 2499 Mark Ryan Guerra

01:23:48
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 second of three lectures introducing the topic of time series analysis, describing multivariate time series, representation theorems, and least-squares estimation. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 18255 MIT OpenCourseWare

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Views: 296 James K

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It covers in detail various methods of measuring trend like Moving Averags & Least Square. Lecture by: Rajinder Kumar Arora, Head of Department of Commerce & Management

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Time Series Analysis PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech Unit 5: Other Time Series Methods Part 2: Multivariate Time Series Modelling Lesson: 3 - State Space Modelling - R example Notes, Code, Data: https://tinyurl.com/Time-Series-Analysis-NotesData
Views: 171 Bob Trenwith

11:48
Forecasts are critical in many fields, including finance, manufacturing, and meteorology. At Uber, probabilistic time series forecasting is essential for marketplace optimization, accurate hardware capacity predictions, marketing spend allocations, and real-time system outage detection across millions of metrics. In this talk, Franziska Bell provides an overview of classical, machine learning and deep learning forecasting approaches. In addition fundamental forecasting best practices will be covered. This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl If you are a software engineer that wants to learn more about machine learning check our dedicated introductory guide https://bit.ly/2HPyuzY . For more awesome presentations on innovator and early adopter, topics check InfoQ’s selection of talks from conferences worldwide http://bit.ly/2tm9loz
Views: 18807 InfoQ

35:29
www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. We aim to be an accessible, community-driven conference, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1596 PyData

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This is a ~3-minute video highlight produced by undergraduate students Charlie Tian and Christina Coley regarding their research topic during the 2017 AMALTHEA REU Program at Florida Institute of Technology in Melbourne, FL. They were mentored by doctoral student Kaylen Bryan and professor Dr. Adrian Peter (Engineering Systems Department). More details about their project can be found at http://www.amalthea-reu.org.

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A three-part presentation from our talk at #earlconf2017 covering financial analysis with tidyquant, time series machine learning with timekit, and enterprise solutions with Business Science. Essentially three presentations in one!