Spend money on US Shares of stock from Indian subcontinent is that All the facts in 10 minutes of time.

Investing in US share markets has garnered excess attention over the last few months. Amazon, Tesla, Apple, Facebook, Google – all stocks have given fabulous returns over the last six months. In this video, we answer the following questions
– What are the rules pertaining to investing in US stocks?
– How can one invest in US shares?
– What are the costs involved in buying US shares?
– What are the taxation rules on US shares?
– Should you invest in US shares?
– What are the alternatives to investing in US shares directly?

HDFC, ICICI, Axis, Vested, Saxo Bank, Stockal, Drive Wealth,

Visit our website www.alreadylate.com to know about our services

#nyse #nasdaq #investing #personalfinance #usa #amazon #apple #facebook #google #netflix
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Speculation with the use of RapidMiner and WEKA on the same facts

Prediction with RapidMiner and WEKA on the same data

This movie shows how to use RapidMiner or WEKA for prediction. It is also showen, how the binning is done. The used dataset could be found on: https://catalog.data.gov/dataset/allegheny-county-property-sale-transactions
Video Rating: / 5

Visualizing Time-Series Facts by using Coffee | DZone.com Encounter

In this presentation, Daniella Pontes, Senior Manager of Product Marketing at InfluxData, and Ivan Kudibal, Engineering Manager at Bonitoo.io, will talk about an application monitoring solution that has been built on top of InfluxDB in order to monitor some user events, selected application events, and error notifications. This application is being used by Web Shop Fly, a company that has grown into the big flight ticket selling travel agency in more than 5 European countries and Russia. Although the IT services are supported in parallel by Google Analytics, Hotjar, or AWS Cloudwatch, InfluxDB put all the important metrics under one umbrella and provide a very exact and transparent way to find the metrics useful in UX improvements, fast response to anomalies, and order management or monitoring of quality of flight data.

Hosts: Daniella Pontes, Influxdata
Ivan Kudibal, Bonitoo.io

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Video Rating: / 5

Facts Technology through Stage Simply by how to Valuable time Assortment Predicting

For downloadable versions of these lectures, please go to the following link:

http://www.slideshare.net/DerekKane/presentations
https://github.com/DerekKane/YouTube-Tutorials

This lecture provides an overview of Time Series forecasting techniques and the process of creating effective forecasts. We will go through some of the popular statistical methods including time series decomposition, exponential smoothing, Holt-Winters, ARIMA, and GLM Models. These topics will be discussed in detail and we will go through the calibration and diagnostics effective time series models on a number of diverse datasets.
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This video shows the usage and performance for the genetic algo. S&P500 Index daily chart with RSI and cycles combination. GA evolves possible trading systems.

Nathaniel Cook – Forecasting Time Combination of Facts at size with the TICK grow

Description
Forecasting time series data across a variety of different time series comes with many challenges. Using the TICK stack we demonstrate a workflow that helps to overcome those challenges. Specifically we take a look at the Facebook Prophet procedure for forecasting business time series.

Abstract
Forecasting time series data can require a significant amount of attention in order to get reliable results. As the number and variety of time series increases it becomes too expensive to manage each forecast individually. Using the TICK stack we demonstrate a workflow that helps to reduce the amount of attention each forecast needs. This is accomplished by using the procedure called Prophet, which was recently open sourced by Facebook.

The basic idea of this procedure is two fold:

Reduce the amount of effort to train and maintain a single forecast.
Automatically surface the forecasts that are performing poorly.
To reduce the amount of effort per forecast, the Facebook Prophet algorithm is a simple model with a few well understood parameters. By automatically surfacing forecasts that perform poorly, effort need only be spent when specific forecasts need attention.

The last piece needed to make this process scale is a single set of tools that follow the workflow. We demonstrate how the TICK stack can be used to manage forecasting time series at scale, using InfluxDB to store the data and Kapacitor to manage and surface forecasts.

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.

Performance first: Facts partitioning on time assortment

Data partitioning is a fundamental step in predictive modeling. For time series, partitioning is done differently from cross-sectional data.

This video supports the textbook Practical Time Series Forecasting.

http://www.forecastingbook.com
http://www.galitshmueli.com
Video Rating: / 5

Cost Ackman: All the facts About A mortgage and Committing to Under an Hour

William Ackman: Everything You Need to Know About Finance and Investing in Under an Hour.

WILLIAM ACKMAN, Activist Investor and Hedge-Fund Manager

We all want to be financially stable and enjoy a well-funded retirement, and we don’t want to throw out our hard earned money on poor investments. But most of us don’t know the first thing about finance and investing. Acclaimed value investor William Ackman teaches you what it takes to finance and grow a successful business and how to make sound investments that will get you to a cash-comfy retirement.

The Floating University
Originally released September 2011.

Additional Lectures:
Michio Kaku: The Universe in a Nutshell

Joel Cohen: An Introduction to Demography (Malthus Miffed: Are People the Problem?)

Steven Pinker: Linguistics as a Window to Understanding the Brain http://www.youtube.com/watch?v=Q-B_ONJIEcE

Leon Botstein: Art Now (Aesthetics Across Music, Painting, Architecture, Movies, and More.)

Tamar Gendler: An Introduction to the Philosophy of Politics and Economics

Nicholas Christakis: The Sociological Science Behind Social Networks and Social Influence

Paul Bloom: The Psychology of Everything: What Compassion, Racism, and Sex tell us about Human Nature

Saul Levmore: Monopolies as an Introduction to Economics

Lawrence Summers: Decoding the DNA of Education in Search of Actual Knowledge

Douglas Melton: Is Biomedical Research Really Close to Curing Anything?

Valuable time Combination of In R | Time frame Series Predicting | Valuable time Group Interpretation | Facts Technology Courses | Edureka

( Data Science Training – https://www.edureka.co/data-science )
In this Edureka YouTube live session, we will show you how to use the Time Series Analysis in R to predict the future!

Below are the topics we will cover in this live session:

1. Why Time Series Analysis?
2. What is Time Series Analysis?
3. When Not to use Time Series Analysis?
4. Components of Time Series Algorithm
5. Demo on Time Series
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LSTM Neural Networks like twitter by the due date Show Guess how to IoT Facts Skill Internet meeting that in fact Jakob Aungiers

Data Science for IoT Conference – London – 26th Jan 2017.
Jakob Aungiers discussing the use of LSTM Neural Network architectures for time series prediction and analysis followed by a Tensorflow/Keras/Python demo.

Slides: https://goo.gl/j9jH4X

GitHub Project: https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction

In reference to blog article: http://www.Jakob-Aungiers.com/articles/a/LSTM-Neural-Network-for-Time-Series-Prediction