- published: 11 Jul 2011
- views: 5823
Data (/ˈdeɪtə/ DAY-tə, /ˈdætə/ DA-tə, or /ˈdɑːtə/ DAH-tə) is a set of values of qualitative or quantitative variables; restated, pieces of data are individual pieces of information. Data is measured, collected and reported, and analyzed, whereupon it can be visualized using graphs or images. Data as a general concept refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing.
Raw data, i.e. unprocessed data, is a collection of numbers, characters; data processing commonly occurs by stages, and the "processed data" from one stage may be considered the "raw data" of the next. Field data is raw data that is collected in an uncontrolled in situ environment. Experimental data is data that is generated within the context of a scientific investigation by observation and recording.
The Latin word "data" is the plural of "datum", and still may be used as a plural noun in this sense. Nowadays, though, "data" is most commonly used in the singular, as a mass noun (like "information", "sand" or "rain").
A time series is a sequence of data points that
1) Consists of successive measurements made over a time interval
2) The time interval is continuous
3) The distance in this time interval between any two consecutive data point is the same
4) Each time unit in the time interval has at most one data point
Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.
Non-Examples: The height measurements of a group of people where each height is recorded over a period of time and each person has only one record in the data set.
Panel data set is sometimes difficult to be differentiated from time series data set. One data set may exhibit both characteristics of panel data set and time series data set. One way to differentiate is to ask: what makes one data record unique from the other records? If the answer is the time data field, then this is a time series data set candidate. If determining a unique records requires a time data field and an additional identifier which is unrelated to time (student ID, stock symbol, country code), then it is a panel data candidate. If the differentiation lies on the non time identifier, then the data set is a cross sectional data set candidate.
Level 2 or Level II may refer to:
Samsung Galaxy (stylized as Samsung GALAXY or SAMSUNG Galaxy) is a series of mobile computing devices designed, manufactured and marketed by Samsung Electronics. The product line includes the Galaxy S series of high-end smartphones, the Galaxy Tab series of tablets, the Galaxy Note series of tablets and phablets with the added functionality of a stylus and the first version of the Galaxy Gear smartwatch, with later versions dropping the Galaxy branding.
Samsung Galaxy devices have traditionally used the Android operating system produced by the Open Handset Alliance led by Google, usually with a custom user interface called TouchWiz. This tradition was broken at CES 2016 with the announcement of the first Galaxy-branded Windows 10 device, the Samsung Galaxy TabPro S.
Since August 2015, all the smartphones of the Galaxy series are categorized in the following way:
The model number of Samsung Galaxy devices will indicate the variant:
Hard Reset is a first-person shooter for Microsoft Windows, developed by Flying Wild Hog. The game features a cyberpunk plot. It draws inspiration from the works of William Gibson, Neal Stephenson, and Philip K. Dick (especially Blade Runner). In 2012, Hard Reset received a free expansion titled Hard Reset: Exile, and was then bundled as Hard Reset: Extended Edition.
Major Fletcher of the CLN protects the futuristic city of Bezoar against attacks from the Barrens, territory conquered by machines.
Hard Reset is modeled on "old school" video games such as Quake and Unreal, which results in more straightforward gameplay than most modern first-person shooters. The various stages have secret areas with hidden pick ups such as health and ammunition. The environments are designed similarly, as there are explosive barrels and various vending machines outfitted with electro-shock anti-vandalism defenses, that can trigger splash damage by being shot at, scattered throughout the levels, which the player can use by luring enemies near them. The game lacks a multiplayer mode, which was a chief criticism.
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Tokenizing real-time data - Streamr explained in 2 minutes
Lecture 2: Time Series analysis. The Nature of Time Series Data and Components of a Time Series - 2
Working with time-series data - Part 2
Level 2 - How to Use Level 2 With Time and Sales Data
Introduction to level 1, level 2 and time and sales data
Time Series Forecasting Theory | AR, MA, ARMA, ARIMA | Data Science
Module 3A : SESSION 2: IDENTIFICATION OF TIME SERIES DATA
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Working with SCADA Data Part 2 - Time Series Field Data
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Streamr CEO Henri Pihkala explains Streamr in 2 minutes. Streamr tokenises real-time data and delivers unstoppable data to unstoppable apps. For more information, visit the following links: Website: https://www.streamr.com Blog: https://blog.streamr.com Slack: https://slack.streamr.com Twitter: https://twitter.com/streamrinc White paper: https://www.streamr.com/whitepaper
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This is part 2 of my time-series data videos. In which I talk more about the reporting and visualizations I done in dashboard, which is mainly the enhancement of the dynamic chart functionality. This video became longer than usual, probably lot more detail on how the code works. So grab a popcorn before you start. Link to the node-red flow shown in the video: http://flows.nodered.org/flow/0e3b9657f9611459f354b61b766a1eaa
In this level 2 time and sales tutorial you'll learn how to use level 2 in conjunction with time and sales data when determining your entry and exit strategies. Click the link below to join the Bullish Bears community, trading room, receive our trade alert setups, and daily watch lists: https://bullishbears.com/ Related Searches: Level 2 stock, nasdaq level 2 quotes, level 2 stock quotes real time
Level 1 data is the most common market data and represents the lowest bid and highest ask data available at the time. Unlike level 2 data, a level 1 quote does not disclose the identity of the buyers and sellers of a security, nor does it show how many shares the market maker is seeking. Level 2 data provides transparency to real supply and demand for a security and shows the price and size that buyers or sellers are willing to pay or sell the stock at. Level 2 data is derived from the level 2 data feed and is constructed based on your subscription to various exchanges. Time and sales provides historical data that can be leveraged to make a guided trading decision. It shows the actual executions (including the time of the trade, price, order size, and order condition) that have taken pla...
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 : analyticsuniversity@gmail.com 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 Mo...
Angular 2 real time chart with Apache Kafka Functionality: Show the real-time data in the chart using Angular 2 application Building Blocks: 1. Client components: • Angular2 • High chart (Angular 2 npm package) • no-kafka (npm package) 2. Service Provider: • Node service with Express JS and socket.io 3. Real time data provider: • Apache Kafka with Zookeeper Prerequisite: 1. Node server installed 2. Angular cli installed 3. Kafka service installed (To install the Kafka service, please refer the above link) 4. Create a 2 different kafka topics 5. Run the kafka provider Steps: 1. Get the sample package from the link given below a. Download the code 2. Install the following package. • npm install • npm install angular2-highcharts –save • npm install no-kafka 3. Run the Zookeeper server ( ...
How to plot more than one data series at a time in MATLAB, including how to make it pretty and add a legend.
Oradea-Sibiu-Bucuresti
I've gone past the limits, I decided to include this too just so you would get an idea of what happens if you go past 21 million.
Bentley's Dr. Tom Walski demonstrates the use of the Time Series Field Data feature to view SCADA data in a model. This is part two of a three part series. Part three demonstrates the use of the SCADA Element. Applicable products: WaterCAD, WaterGEMS, SewerCAD, SewerGEMS, CivilStorm, PondPack Learn More: https://communities.bentley.com/products/hydraulics___hydrology/w/hydraulics_and_hydrology__wiki/20232.how-do-i-enter-time-series-field-data-and-add-it-to-my-graph https://communities.bentley.com/products/hydraulics___hydrology/w/hydraulics_and_hydrology__wiki/2645.importing-time-series-data-using-modelbuilder-tn https://www.youtube.com/watch?v=YgnBFzc46vE https://youtu.be/nnPEXwUoAGM
Eric Torkia, the analytics practice lead at Technology Partnerz presents a comprehensive overview of timeseries forecasting with Palisade @Risk's Monte Carlo simulation for Microsoft Excel. Topics will include: Linear Regression and Curve Fitting Autoregressive models Random Walks Trend Charts Real Options Moving Averages For more information and articles, please visit http://www.crystalballservices.com/Resources/ConsultantsCornerBlog.aspx
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This video will show you how to create and load dataset in weka tool. weather data set excel file https://eric.univ-lyon2.fr/~ricco/tanagra/fichiers/weather.xls
Space and time are inseparable, and integrating the temporal aspect of your data into your spatial analysis leads to powerful discoveries. This workshop will build on the cluster analysis methods discussed in Spatial Data Mining I by presenting advanced techniques for analyzing your data in the context of both space and time. We will cover space-time pattern mining techniques including aggregating your temporal data into a space-time cube, emerging hot spot analysis, local outlier analysis, best practices for visualizing your space-time cube, and strategies for interpreting and sharing your results. Come learn how to use these new techniques to get the most out of your spatiotemporal data.
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This is part 2 of my time-series data videos. In which I talk more about the reporting and visualizations I done in dashboard, which is mainly the enhancement of the dynamic chart functionality. This video became longer than usual, probably lot more detail on how the code works. So grab a popcorn before you start. Link to the node-red flow shown in the video: http://flows.nodered.org/flow/0e3b9657f9611459f354b61b766a1eaa
In this webinar, Michael DeSa will define what time series data is (and isn't), how the problem domain time series differs from more traditional data workloads like full-text search, and examine how InfluxData is differentiated from other proposed solutions. Read more about the components, guides, concepts, and tools that make up our InfluxDB: https://docs.influxdata.com/influxdb/v1.3/introduction/ In this video, you will learn: - What is time series data - Differences between time-series databases (TSDBs) - InfluxDB data model New to the time series data metrics? Try us out for free for 14 days! https://portal.influxdata.com/
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 : analyticsuniversity@gmail.com 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 Mo...
Space and time are inseparable, and integrating the temporal aspect of your data into your spatial analysis leads to powerful discoveries. This workshop will build on the cluster analysis methods discussed in Spatial Data Mining I by presenting advanced techniques for analyzing your data in the context of both space and time. We will cover space-time pattern mining techniques including aggregating your temporal data into a space-time cube, emerging hot spot analysis, local outlier analysis, best practices for visualizing your space-time cube, and strategies for interpreting and sharing your results. Come learn how to use these new techniques to get the most out of your spatiotemporal data.
This is Lecture 4 in my Econometrics course at Swansea University. Watch live on The Economic Society Facebook page Every Monday 2:00 pm (UK time) October 2nd - December 2017. https://www.facebook.com/TheEconomicSociety In this lecture, I explain different types of dynamic models. The lecture covered Distributed lag models, Koyck transformation AR process, and Autoregressive Distributed Lag ARDL models. The example we followed throughout the lecture is concerning the estimation of the consumption function. The lecture also covered stationarity in time series, stationary as a concept, what the consequences are of regression non-stationary time series, and how to examine a given series (by graph, correlogram, and unit root tests). We also discussed how to covert a non-stationary series i...
Ajay Kulkarni, CEO/Co-founder from TimescaleDB delivers their talk/keynote, "What the heck is time-series data (and why do I need a time-series database)?", on DAY 3 of the Percona Live Open Source Database Conference 2017, April 27, at Santa Clara, CA. Time-series databases are the fastest growing category in databases today. We even held a keynote panel discussing some of the options earlier in the conference. But what exactly is "time-series data"? And why do we need a special database to handle it? https://www.percona.com/live/17/sessions/what-heck-time-series-data-and-why-do-i-need-time-series-database
-What is Data Binding -How to use Data Binding features in Angular JS -Various types of Bidings in Angular JS (One-Way, Two-Way, One-Time) -How to use ng-click -How to work with ng-bind and ng-model -How to use double color (::) in evaluation expression for one-time binding -How to push values from Scope to view and vice versa in Angular JS Source code: http://techcbt.com/Post/351/Angular-JS-basics/learn-data-binding-in-angular-js-one-way-two-way-one-time
An introduction to basic panel data econometrics. Also watch my video on "Fixed Effects vs Random Effects". Go to my website (www.burkeyacademy.com) under "Files" if you want to download the data. As always, I am using R for data analysis, which is available for free at r-project.org
VCE Further Maths Tutorials. Core (Data Analysis) Tutorial: Smoothing Time Series Data. This tute runs through mean and median smoothing, from a table and straight onto a graph, using 3 and 5 mean & median smoothing and 4 point smoothing with centring. For more tutorials, visit www.vcefurthermaths.com
Oradea-Sibiu-Bucuresti
Wes McKinney In this tutorial, I'll give a brief overview of pandas basics for new users, then dive into the nuts of bolts of manipulating time series data in memory. This includes such common topics date arithmetic, alignment and join / merge method
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
Eric Torkia, the analytics practice lead at Technology Partnerz presents a comprehensive overview of timeseries forecasting with Palisade @Risk's Monte Carlo simulation for Microsoft Excel. Topics will include: Linear Regression and Curve Fitting Autoregressive models Random Walks Trend Charts Real Options Moving Averages For more information and articles, please visit http://www.crystalballservices.com/Resources/ConsultantsCornerBlog.aspx
Speaker: Matvey Arye, Senior Software Engineer, TimescaleDB Abstract: When storing time-series data, many developers start with some well-trusted system like PostgreSQL, but as their data hits a certain scale, give up its query power and ecosystem by migrating to a NoSQL or other "modern" time-series architecture. This trade-off is unnecessary, and we've built TimescaleDB, an efficient, scalable time-series database engineered up from Postgres so that developers aren’t forced into making it. At a high level, the nature of time-series workloads--appending data about recent events--presents different demands than transactional (OLTP) workloads. We've architected our time-series database to take advantage of and embrace these differences. TimescaleDB improves insert rates by 20X over Pos...
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations 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.
Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming. For more information on Databricks Delta, check out announcement here: https://databricks.com/blog/2017/10/2... And learn more about the product itself here: https://databricks.com/product/databr...
BioXFEL Journal Club - Max Weidorn - November 18th, 2015
Predix Time Series is optimized for the efficient storage and fast analysis of continuous streams of sensor data. Join Rich, Rama, and Arvind as they explore this Predix service that is purpose-built for handling machine and other IoT data. LEARN MORE ABOUT GE DIGITAL: https://www.ge.com/digital SUBSCRIBE TO THE GE DIGITAL CHANNEL: https://www.youtube.com/c/GEDigital?sub_confirmation=1 CONNECT WITH GE DIGITAL ONLINE: Visit GE Digital’s Website: https://www.ge.com/digital/ Follow GE Digital on Twitter: https://twitter.com/GE_Digital Find GE Digital on LinkedIn: https://www.linkedin.com/company/2681277
This video is on Panel Data Analysis. Panel data has features of both Time series data and Cross section data. You can use panel data regression to analyse such data, We will use Fixed Effect Panel data regression and Random Effect panel data regression to analyse panel data. We will also compare with Pooled OLS , Between effect & first difference estimation For Analytics study packs visit : https://analyticuniversity.com Time Series Video : https://www.youtube.com/watch?v=Aw77aMLj9uM&t;=2386s Logistic Regression using SAS: https://www.youtube.com/watch?v=vkzXa0betZg&t;=7s Logistic Regression using R : https://www.youtube.com/watch?v=nubin7hq4-s&t;=36s Support us on Patreon : https://www.patreon.com/user?u=2969403