The document discusses time series forecasting. It defines time series as a sequence of measurements on the same variable collected over time at regular intervals. Examples of time series data include monthly sales, yearly GDP, and quarterly income. Time series forecasting is important for organizations to reduce risks and optimize processes like production and manpower planning. The key challenge with time series data is that observations are not independent and ordered, with dependency between consecutive observations. The objective is to learn time series forecasting techniques.
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This presentation discusses privacy issues related to big data. It notes that the volume of digital data is growing exponentially and will exceed 40 zettabytes by 2020. While big data provides opportunities, it also raises privacy challenges since data contains personal information. The presentation examines key privacy principles like user choice and control. It argues the government should establish a general privacy framework but not over-regulate data collection, to encourage investment. Government could also utilize collected data to create open data platforms and boost the economy.
Intro questionWhy do Mearsheimer and Walt claim that the War in .docxnormanibarber20063
Intro question
Why do Mearsheimer and Walt claim that the War in Iraq was unnecessary? Do you agree with the reasoning for them deeming the invasion unnecessary(use the readings to state your point)
Re: Intro question
Walt and Mearsheimer claim that the war in Iraq is unnecessary because they feel that America is under the false impression that Saddam Hussein is much more reckless than he is. They state that we believe he cannot be deterred from the use of Weapons of Mass Destruction (WMD), when in fact he actually can. They mention that although he has been in 2 wars in the past thirty years, that this is no worse than neighboring countries "such as Egypt or Israel" and that "a careful look will determine that his behavior is far from reckless".
I would say that I agree, to an extent, with their reasoning to deem the invasion unnecessary. They say that due to the fact that he has been far from reckless in the past, we should assume that he will maintain this behavior-- and while this view point may make sense to some; I feel as though it is entirely too optimistic
You are asked to read the content in Week 6 folder and respond to either the intro question or add an idea or a comment to the response. 3-4 sentences only
mearsheimer and walt.pdf
Project Management Case
You are working for a large, apparel design and manufacturing company, Trillo Apparel Company (TAC), headquartered in Albuquerque, New Mexico. TAC employs around 3000 people and has remained profitable through tough economic times. The operations are divided into 4 districts; District 1 – North, District 2 – South, District 3 – West and District 4 – East. The company sets strategic goals at the beginning of each year and operates with priorities to reach those goals.Trillo Apparel Company Current Year Priorities
· Increase Sales and Distribution in the East
· Improve Product Quality
· Improve Production in District 4
· Increase Brand Recognition
· Increase RevenuesCompany Details
Company Name: Trillo Apparel Company (TAC)
Company Type: Apparel design and production
Company Size: 3000 employees
Position
# Employees
Owner/CEO
1
Vice President
4
Chief Operating Officer
1
Chief Financial Officer
1
Chief Information Officer
1
IT Department
38
District Manager
4
Sales Team
30
Accountant
12
Administrative Assistant
7
Order Fullfilment
45
Customer Service
57
Designer
24
Project Manager
10
Maintenance
25
Operations
2500
Shipping Department
240
Total Employees
3000
Products: Various Apparel
Corporate Location: Albuquerque, New MexicoTAC Organization Chart
District 4 Production Warehouse Move Project Details
The business has expanded considerably over the past few years and District 4 in the East has outgrown its current production facility. Because of this growth the executives want to expand the current facility, moving the whole facility 10 miles away. The location selected has enough room for the production and the shipping department. Howev.
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Simon Property Group
Angel Bloodworth
Strategic Planning for Organizations MGT450
University of Arizona
14 March 2022
Achieving a high level of financial stability while operating a profitable company is one of the most challenging tasks a business can face. After all, any firm facing cash flow and budgetary challenges will eventually collapse if these issues are not handled as soon as possible. One organization that has been having financial issues recently is Simon Properties Group. The company's financial woes, which partly has been caused by Covid-19, have damaged the company's reputation, and the public is slowly losing trust in the company's capabilities. Additionally, the fear of bankruptcy has adversely affected the company's long-term creditworthiness. This paper necessitates an analysis of Simon Properties Group, including its leadership, potential competition, and a recent news item posing a challenge to its strategy.
Organization
Established in the United States, Simon Property Group is a real estate investment trust specializing in outlet malls, retail malls, and lifestyle complexes. The company was founded in 1982 and currently has its headquarters in Indianapolis, Indiana. The Simon Property Group was founded in Indianapolis by brothers Herbert and Melvin Simon, who started by developing strip malls in the city. The company has locations around Europe, North America, and Asia, where the firm serves thousands of people every day and earns millions of dollars in sales each year. The company's portfolio includes properties that have gained national and international attention - assets that have proven to be the preferred destination for retailers (Jie & Jianwei, 2021). Simon is also known for its strong financial position, a senior management team that has been in place for many years and is highly regarded, as well as its innovative mindset, which is reflected in the company's history.
The industry
The corporation operates in the real estate business. Real estate has a lengthy history in the United States. The federal government sold and gave the property to private individuals for their own use after the Revolutionary War when it was no longer under the control of England. As the nation grew westward, this practice continued, most notably with the passage of the Homestead Act in 1862, which authorized individual ownership of U.S. property in return for maintaining and developing the area for at least five years (Katzler, 2017). Through the Homestead Act, the United States government granted more than 300 million acres of public land to private landowners, laying the groundwork for the real estate industry, which is currently worth $203.1 billion.
Mission and Vision
The company’s mission is to become the top retail real estate developer, owner, and manager globally.
The company's vision statement is that it wants to be the unchallenged leader in the business.
Values and purpose
Integrity, innovati ...
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Simon Property Group
Angel Bloodworth
Strategic Planning for Organizations MGT450
University of Arizona
14 March 2022
Achieving a high level of financial stability while operating a profitable company is one of the most challenging tasks a business can face. After all, any firm facing cash flow and budgetary challenges will eventually collapse if these issues are not handled as soon as possible. One organization that has been having financial issues recently is Simon Properties Group. The company's financial woes, which partly has been caused by Covid-19, have damaged the company's reputation, and the public is slowly losing trust in the company's capabilities. Additionally, the fear of bankruptcy has adversely affected the company's long-term creditworthiness. This paper necessitates an analysis of Simon Properties Group, including its leadership, potential competition, and a recent news item posing a challenge to its strategy.
Organization
Established in the United States, Simon Property Group is a real estate investment trust specializing in outlet malls, retail malls, and lifestyle complexes. The company was founded in 1982 and currently has its headquarters in Indianapolis, Indiana. The Simon Property Group was founded in Indianapolis by brothers Herbert and Melvin Simon, who started by developing strip malls in the city. The company has locations around Europe, North America, and Asia, where the firm serves thousands of people every day and earns millions of dollars in sales each year. The company's portfolio includes properties that have gained national and international attention - assets that have proven to be the preferred destination for retailers (Jie & Jianwei, 2021). Simon is also known for its strong financial position, a senior management team that has been in place for many years and is highly regarded, as well as its innovative mindset, which is reflected in the company's history.
The industry
The corporation operates in the real estate business. Real estate has a lengthy history in the United States. The federal government sold and gave the property to private individuals for their own use after the Revolutionary War when it was no longer under the control of England. As the nation grew westward, this practice continued, most notably with the passage of the Homestead Act in 1862, which authorized individual ownership of U.S. property in return for maintaining and developing the area for at least five years (Katzler, 2017). Through the Homestead Act, the United States government granted more than 300 million acres of public land to private landowners, laying the groundwork for the real estate industry, which is currently worth $203.1 billion.
Mission and Vision
The company’s mission is to become the top retail real estate developer, owner, and manager globally.
The company's vision statement is that it wants to be the unchallenged leader in the business.
Values and purpose
Integrity, innovati ...
Data Quality Considerations for CECL MeasurementLibby Bierman
This webinar covers how institutions should be getting their data ready for the Current Expected Credit Loss Model, CECL, which will be the new standard for the ALLL or allowance for loan and lease losses.
Find out more at alll.com.
The document provides instructions for requesting writing assistance from HelpWriting.net. It outlines a 5-step process: 1) Create an account with a password and email. 2) Complete a form with assignment details. 3) Review bids from writers and choose one. 4) Review the completed paper and authorize payment. 5) Request revisions to ensure satisfaction, with a refund option for plagiarized content.
The document is a handbook on Solvency II data management published by A-Team Group. It provides an overview of Solvency II, explaining that the regulation aims to harmonize European insurance regulation and create a stable industry driven by risk management. It impacts insurers, asset managers, and custodians. The three pillars of Solvency II (capital requirements, governance/supervision, and reporting) create significant data management requirements. Insurers must have access to granular pricing, valuation, and reference data to meet requirements under the pillars. Asset managers must also provide transparency on investments held on behalf of insurers to meet "look-through" requirements. The handbook examines the various data issues and challenges
Here are a few key points about how King and Queens Consignment could benefit the local economy:
- Job creation: As the business grows, it will provide employment opportunities for local residents, helping reduce unemployment. Even starting with just a few employees helps the economy.
- Increased spending: Both customers and employees will spend more at other local businesses, like restaurants, shops, etc. This multiplier effect stimulates additional economic activity.
- Tax revenue: As a business, King and Queens will pay various taxes like property tax, sales tax, payroll tax. This tax revenue can be used by the city/county for infrastructure, services, schools and other programs that benefit the community.
- Attract customers: A new
The latest edition of BIZGrowth Strategies includes the following articles: Business Analytics: Big Data Now a Tool for Small Business; How the ACA Impacts Your Tax Return; Why No Exit Plan is a Bad Plan; Eight 403(b) Mistakes You Can Avoid; and Why Professional Service Companies Should Care about Personal Brands.
This document discusses big data and provides an overview of key concepts:
- Big data is defined as datasets that are too large or complex for traditional data management tools to handle. It is characterized by volume, velocity, and variety.
- Big data comes from a variety of sources like social media, sensors, web logs, and transaction systems. It is growing rapidly due to the digitization of information.
- Big data can be used for applications like enhancing customer insights, optimizing operations, and extending security and intelligence capabilities. Example use cases are described.
- Architecting solutions for big data requires handling its scale and integrating diverse data types and sources. Both traditional and new analytics approaches are needed.
Public Sector - United States - How to Transform Government - April 2022.pptxpaul young cpa, cga
Paul Young provides an overview of key issues facing the public sector in the United States. He discusses a CBO report that finds the federal budget deficit is improving but long-term fiscal challenges remain. Corporate tax changes and a global minimum tax rate are addressed. High levels of federal, state, and local government debt are also discussed. The document outlines Paul's expertise and provides links to further information on topics like audits, ESG reporting, risk management, and governance challenges across different levels of government.
Manufacturing has gone through many changes over the past 40 years. MFG are always looking at better ways to make their products as well as deal with consumer trends.
Decline in manufacturing has led to less union jobs. Major unions need to change their approach including how they managed training and skills development for their members.
Public Sector - United States - How to Transform Government - July 2022.pptxpaul young cpa, cga
All levels of government are facing many challenges including taxation reforms, delivery program spending with value for money, high inflation, fiscal gap, infrastructure gap, and the ability to mitigate the impacts of climate change.
Driss temsamani Big Data Marketing Innovation Summit 2015 KeynoteDriss R. Temsamani
This document discusses strategies for managing big data. It begins by outlining the exponential growth in data creation and highlights the need for a well-defined data management strategy. Several statistics on data creation are provided. The rest of the document discusses challenges posed by the volume, variety and velocity of big data, as well as opportunities it presents for transforming insights into opportunities across various industries. Specific competitive advantages of capitalizing on big data for sales, research and development, product development, and other areas are outlined. The closing section identifies five areas to focus on for taming big data.
Driss temsamani big data marketing innovation summit 2015Driss R. Temsamani
This document discusses strategies for managing big data. It begins by outlining the exponential growth in data creation and highlights the importance of having a well-defined data management strategy. Several statistics are provided about data creation from various sources. The rest of the document discusses challenges posed by the 3Vs of big data (volume, variety and velocity) and how big data can provide competitive advantages if leveraged properly across various business functions like sales, R&D, production and marketing. Specific opportunities mentioned include improving customer segmentation, developing new products and optimizing supply chains.
This document provides a usage guide for Mergent Online, a global business and financial database. It describes the various search and data modules available, including basic and advanced searching, U.S. and international company data, annual reports, and other financial information. Key features and functions are outlined for finding company information and building customized searches across multiple databases.
The results from Syncsort’s annual State of the Mainframe Survey are in! The importance of mainframe data is rising as a critical component of enterprise-wide strategies that leverage modern data architectures for Big Data analytics and security and compliance. Join us for a live webcast and Q&A as we take you through an in-depth look at the survey results and the four trends to watch for in 2017. You’ll also learn how for their organizations your peers view:
• The future of mainframe and mainframe-related budgets
• The importance of Mainframe data for Big Data analytics
• The need for operational intelligence and security intelligence for the mainframe
• How IT priorities are shifting (and where)
Public Sector - United States - How to Transform Government - October 2022.pptxpaul young cpa, cga
Blog – How to Transform the Public Sector Fiscal and Governance Model – The United States – November 2022
Governments around the world are struggling with high inflation - https://www.bloomberg.com/news/articles/2022-12-08/us-federal-reserve-s-inflation-fight-spurs-crypto-tech-housing-market-drops
Tax reforms continue to be a challenge for all levels of government - https://www.atlanticcouncil.org/in-depth-research-reports/report/improving-tax-policy-in-lac-a-balancing-act/
More needs to be done to expand performance and operational audits across all levels of government.
GDP growth will continue to challenge all levels of government - https://www.ft.com/content/24dbcc0f-7974-48d7-9824-ab86b58a3a29 or https://www.gold.org/goldhub/research/gold-outlook-2023-global-economy-crossroads
Dr. Awny Alnusair University of the Cumberlands1.docxmadlynplamondon
Dr. Awny Alnusair
University of the Cumberlands
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Putting the Data Analytics Lifecycle into Practice
The Data Analytics Lifecycle consists of the following six phases:
Discovery
Data Preparation
Model Planning
Model building
Communicate Results
Operationalize
To begin analyzing the data, you will need a tool that allows you to look closely at the data – That is “R”
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KDnuggest Poll - 2019
KDnuggets Poll is a survey of data science and machine learning software. It asks programmers what languages they use on a regular basis in their work
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The R Project for Statistical Computing
First of all you need to get R installed on your computer
https://www.r-project.org/
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Up and Running with R
Once R is installed, you can test the installation by opening the R Console
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RStudio – an IDE for R
https://www.rstudio.com/
https://rstudio.cloud/
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Entering Data into R
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R Packages
https://cloud.r-project.org/web/packages/index.html
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Things you’r expected to know about R ….
R Data Types and structures
Basic descriptive statistics, dirty data ..
Data Visualization and relationships between multiple variables
Generic Functions
Dealing with sample datasets that are available for you
Statistical Methods for Model Building and Evaluation
Hypothesis Testing - Welche’s t-test, Confidence intervals, Wilcoxon rank-sum test, type I and II errors, and ANOVA
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Example 1
Summary
On September 18, Donald Trump announced that he is not allowing states to set their own vehicle emission standards by revoking California’s Federal Waiver. These actions come as a shock to a lot of people. Republicans are generally all for protecting states rights. Many states began to follow suit of California and since car-makers are not going to make different cars to meet different standards, it ended up becoming a sort of national standard. Many are worried that these unprecedented actions are an attack on states rights. Trump hopes that by setting federal standards that are less strict on vehicle emissions, safer cars will become more affordable. Some opposers, like Jeff Alson, believes that Trump is "doing more to destroy the planet than any other president in history."
Connection
The top 3 importers of cars into the U.S. are Japan, Germany, and South Korea respectively. Following the Obama-era standard, auto makers are required to produce vehicles that have an average fuel economy of almost 55 mpg by 2025. With Trump's policy that number would go down to 37 mpg. The U.S. election is coming up, and if a Democrat is elected into office, it is likely that the regulation might change again. Germany and Japan's largest exports are vehicles, so they already have to take into consideration the legislation of many other countries. Having all of these different regulations make things confusing and difficult for vehicle makers. Germany is already going through economic struggles. Auto sales have continuously dropp ...
"Alpha from Alternative Data" by Emmett Kilduff, Founder and CEO of Eagle AlphaQuantopian
From QuantCon 2017: At J.P. Morgan's annual quantitative conference 93% of investors said alternative data will change the investment landscape.
In this presentation, Emmett will discuss the rapidly increasing adoption of alternative data, give a detailed overview of the 24 different types of alternative data, outline the applications of alternative data for quantitative funds, discuss interesting datasets that are available (including Asian datasets) and present case studies that evidence value in alternative datasets.
Similar to TIMES SERIES FORECASTING ON HISTORICAL DATA IN R (20)
SMOTE and K-Fold Cross Validation-Presentation.pptxHaritikaChhatwal1
SMOTE is a technique used to handle class imbalance problems in data. It involves over-sampling the minority class by synthesizing new minority class examples and under-sampling the majority class. This helps improve recall, or the detection of truly positive instances from the minority class, which is often prioritized over precision in class imbalance situations. K-fold cross-validation is a resampling method used to evaluate machine learning models on limited data. It involves splitting the dataset into k groups, using each group as a test set while the remaining form the training set, and averaging the results.
This document discusses factor analysis, a technique used to identify underlying dimensions or factors within a set of variables. It provides definitions of key terms like factor loadings, communality, scree plot, and factor scores. It also presents an example factor analysis using data on salespeople. The results show unrotated and rotated factor loadings, variance summarized by each factor, and issues that can arise in interpreting factor analysis outputs. Applications mentioned include using factor analysis in questionnaire design and customer profiling.
This document discusses various statistical concepts including measures of central tendency, probability, probability distributions, and inferential statistics techniques. It provides examples of how to identify the appropriate probability or distribution technique to use for a given problem, including the binomial, Poisson, and normal distributions. Key steps outlined include identifying the problem type, determining if it involves discrete or continuous data, and checking for conditions that indicate applying concepts like conditional probability or Bayes' theorem.
This document outlines the problem statement and objectives of a study to analyze loan data and identify borrower characteristics that contribute to delinquency. The objectives are to understand the major factors leading borrowers to become delinquent, as delinquency increases risk of default. The main objective is to minimize this risk by building a decision tree model using CART technique to identify risk and non-risk attributes of borrowers that result in delinquency. The tool to be used is R Studio.
Frequency Based Classification Algorithms_ importantHaritikaChhatwal1
This document discusses two types of frequency-based classification methods: K-Nearest Neighborhood (KNN) and Naive Bayes. KNN is a simple counting-based method that measures distances between data points to classify them, but can be ad-hoc. Naive Bayes uses Bayes' theorem to calculate conditional probabilities of class membership given attribute values in order to classify data points into classes. It makes the assumption that attributes are conditionally independent given the class.
This document provides an overview of descriptive statistics concepts. It discusses different data types, measurement scales, graphical and tabular data representations, and methods for summarizing data distributions. The agenda outlines topics including descriptive statistics graphs and tables, measures of central tendency like mean, median and mode, measures of variation such as range and standard deviation, and probability distributions. Descriptive statistics are used to organize and describe characteristics of data through quantitative methods.
This document provides an introduction and overview to learning R. It covers installing R and RStudio, basic data types and structures like vectors, matrices and data frames. It also discusses importing data, viewing and manipulating data through functions like filtering, binding and transforming. Finally, it discusses creating summary tables from data, joining datasets, and creating visualizations and plots in R using packages like ggplot2. The goal is to learn the basics of working with data in R, performing basic analysis and creating charts.
R is an open source programming language used for data science and statistical computing. The document discusses the basics of R programming including data types, operators, control structures, functions, and data frames. It also covers R libraries, graphics, statistical analysis techniques, and how to import and export data. R can be used for tasks like classification, time series analysis, clustering, modeling, and creating visualizations. It is available free of charge and can be integrated with other programming languages.
This document discusses the nature of financial management. It begins by explaining that financial management deals with the management of capital flows and financial decision making, including financing and investing. It then outlines the scope of the finance function, including financial planning, raising funds, investment decisions, working capital management, and other financial events. The document also discusses the role of the finance manager as an intermediary between the firm's operations and capital markets. Key decisions made by the finance manager are also summarized, including investment, financing, and dividend decisions. The objectives of financial management, including profit maximization and maximization of shareholder wealth, are compared. Risk and return as basic dimensions of financial decisions are also highlighted.
This document contains questions for two chapters on financial management. For chapter 1 on the background of financial management, it includes questions about the objectives of financial management, the role of finance managers, and important financial decisions regarding investments, financing, and dividends. For chapter 2 on the mathematics of finance, it includes questions about concepts like present value, time value of money, effective interest rates, and their relevance for financial decision making. The document provides an overview of the key topics and concepts covered in the introductory chapters on financial management.
This document provides instructions for students to access and complete an ERIC course on social and behavioral sciences. Students should click on the ERIC course access guide in Blackboard, follow the instructions, and sign up for the social and behavioral sciences track. They then need to complete the module, quizzes, survey, and print the certificate.
MED 900 Correlational Studies online safety sake.pptxHaritikaChhatwal1
This study aimed to compare teacher feedback, student self-regulated learning, and the relationship between these factors in high-achieving versus low-achieving secondary schools. Specifically, the study sought to determine if there were differences in (1) the types of mathematics teacher feedback, (2) students' self-regulated learning in mathematics, and (3) the relationships between teacher feedback and student self-regulated learning between high- and low-achieving schools. The study was motivated by research suggesting school climate and culture can impact these factors differently in high versus low performing schools.
Correlational research establishes relationships between variables without determining cause, using dependent variables only. It demonstrates relationships exist but not causation. A correlation coefficient measures the direction and strength of relationships between two variables on a scale from -1 to 1, with higher positive or negative values indicating stronger linear relationships. Statistical analysis evaluates numerical data through correlation coefficients and scatter plots to describe variable relationships.
Correlational research establishes relationships between variables but does not determine cause-and-effect. It uses correlation coefficients to measure the direction and strength of relationships between two variables. Statistical analysis of scores from each individual on two variables can show their relationship graphically in a scatter plot. Experimental research determines cause-and-effect relationships, while descriptive research explores characteristics without determining relationships.
PE ratio is a metric that compares a company's stock price to its earnings per share. It indicates how much an investor pays for each dollar of earnings. A PE ratio is calculated by dividing the current stock price by the earnings per share. PE ratios help investors compare similar companies and determine if a stock is undervalued, appropriately priced, or overvalued. Factors like growth rates, profit margins, returns, macroeconomic conditions, and intangible assets can impact a company's PE ratio. Comparing a company's PE ratio to its industry peers provides useful insight into how the market values that company.
This thesis examines whether implied volatility from options prices can provide additional information for forecasting realized volatility compared to historical volatility models. The study analyzes the S&P 500 index and VIX in the US, and the Euro Stoxx 50 index and VSTOXX in Europe from 2005 to 2019. GARCH and EGARCH models are estimated with and without implied volatility to evaluate its information content. Out-of-sample forecasts are generated and evaluated using statistical tests. The results suggest that including implied volatility improves model fit but does not necessarily lead to more accurate volatility forecasts compared to historical volatility alone.
A sole trader business is owned and operated by one person. It has few legal requirements beyond registering with tax authorities and adhering to relevant industry laws. Advantages include complete control, keeping all profits, and flexibility. Disadvantages include unlimited liability, limited financing options, and risk if the sole proprietor becomes ill or dies.
1. An entrepreneur organizes and operates a new business venture, taking on risks to produce goods or services. A business plan documents business objectives, operations, finance, and ownership to help obtain loans and guide the business.
2. Businesses want to grow internally through new branches or externally through mergers and acquisitions to benefit from economies of scale, increased market share, and access to new markets. However, growth brings challenges like difficulty controlling larger operations.
3. Not all businesses grow - some stay small due to factors like their industry, market size, or owners' objectives. New businesses are also at high risk of failure due to lack of experience, understanding of the market, sales, and financial resources compared
Amazon DocumentDB(MongoDB와 호환됨)는 빠르고 안정적이며 완전 관리형 데이터베이스 서비스입니다. Amazon DocumentDB를 사용하면 클라우드에서 MongoDB 호환 데이터베이스를 쉽게 설���, 운영 및 규모를 조정할 수 있습니다. Amazon DocumentDB를 사용하면 MongoDB에서 사용하는 것과 동일한 애플리케이션 코드를 실행하고 동일한 드라이버와 도구를 사용하는 것을 실습합니다.
How We Added Replication to QuestDB - JonTheBeachjavier ramirez
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A few years later, data replication —for horizontally scaling reads and for high availability— became one of the most demanded features, especially for enterprise and cloud environments. So, we rolled up our sleeves and made it happen.
Today, QuestDB supports an unbounded number of geographically distributed read-replicas without slowing down reads on the primary node, which can ingest data at over 4 million rows per second.
In this talk, I will tell you about the technical decisions we made, and their trade offs. You'll learn how we had to revamp the whole ingestion layer, and how we actually made the primary faster than before when we added multi-threaded Write Ahead Logs to deal with data replication. I'll also discuss how we are leveraging object storage as a central part of the process. And of course, I'll show you a live demo of high-performance multi-region replication in action.
Airline Satisfaction Project using Azure
This presentation is created as a foundation of understanding and comparing data science/machine learning solutions made in Python notebooks locally and on Azure cloud, as a part of Course DP-100 - Designing and Implementing a Data Science Solution on Azure.
Amazon Aurora 클러스터를 초당 수백만 건의 쓰기 트랜잭션으로 확장하고 페타바이트 규모의 데이터를 관리할 수 있으며, 사용자 지정 애플리케이션 로직을 생성하거나 여러 데이터베이스를 관리할 필요 없이 Aurora에서 관계형 데이터베이스 워크로드를 단일 Aurora 라이터 인스턴스의 한도 이상으로 확장할 수 있는 Amazon Aurora Limitless Database를 소개합��다.
[D3T1S04] Aurora PostgreSQL performance monitoring and troubleshooting by use...
TIMES SERIES FORECASTING ON HISTORICAL DATA IN R
1. TIME SERIES FORECASTING
9-Jan-18 1
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3. Why Forecast?
Every organization faces internal and external risks, such as
high competition, failure of technology, labor unrest, inflation,
recession, and change in government laws.
Every business operates under risk and uncertainty
Forecast is necessary to lessen the adverse effects of risks
There are varied methods of forecast – some of which you
have already covered. Such as
– Regression
– Data mining methods
3
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Sharing or publishing the contents in part or full is liable for legal action.
4. Why Forecast?
Time Series is another technique for forecasting
Why do we need so many techniques for forecasting? Because
not all data are the same, or similar. Because different types of
data possess different features, different methods of forecast
becomes applicable
For example: In regression or CART you have one response
and a number of predictors
4
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5. How to Forecast
• If it is a problem of whether to extend loan facility
to the next applicant, a bank matches profile of the
applicant with their historic data repository and
take a decision based on the likelihood of the
applicant’s loan ‘going bad’!
• The applicant’s demographical information,
economic stability as well as various other
predictors are used for taking the final decision
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6. Monthly Sales of a Shoe Type
Based on the above how to predict sales for the next two years?
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7. How to Forecast?
This is an example of Time Series Data
The objective of this lesson is to learn about
What is time series?
Where do we encounter time series data?
What are the special features of time series data?
What are the typical situations where time series methods
are applied?
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8. What is Time Series?
A time series is a sequence of measurements on the same variable
collected over time.
The measurements are made at regular time intervals.
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9. Monthly Sales of a Shoe Type
Monthly sales volume for
popular type of shoes by a
certain manufacturer
The variable observed is
Sales Volume
It is observed every month
– regular interval
It is observed for full 5
years : 2011 – 2015
This is an example of
monthly TS data
Year Month No. of Pairs
2011 Jan 742
2011 Feb 741
2011 Mar 896
2011 Apr 951
2011 May 1030
2011 Jun 697
… … …
2015 Jul 1119
2015 Aug 783
2015 Sep 901
2015 Oct 1023
2015 Nov 1209
2015 Dec 1013
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10. Yearly GDP of USA
The variable observed is
GDP
It is observed every year
1929 - 1992
This is an example of
yearly TS data
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What is Time Series?
Year US GDP
1929 821.8
1930 748.9
1931 691.3
1932 599.7
……… …………
1990 4739.2
1991 4822.3
1992 4835
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11. What is Time Series?
Quarterly income of a
certain Company
The variable observed is
Revenue
It is observed every
quarter from Q4 2000 to
Q1 2014
This is an example of
quarterly TS data
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Year Quarter
Income (m.
USD)
2000 Q4 3616
2001 Q1 3043
2001 Q2 2778
2001 Q3 2839
2001 Q4 3212
2002 Q1 3297
…… …….. ……..
2012 Q4 5624
2013 Q1 5505
2013 Q2 5122
2013 Q3 5633
2013 Q4 6793
2014 Q1 5130
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12. What is Time Series?
We can think of daily time series –
• daily closing price or daily closing volume of a certain
stock
• Sensex value
• Total daily transaction volume of your nearest ATM
machine
All are examples of time series
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13. What is not a Time Series?
Data collected on multiple items at the same point
of time is not a time series!
Example: DowJones average on a single day
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Date MMM T AXP BA CAT CVX CSCO KO DD XOM
1/9/2012 0.0046 -0.0094 0.0037 0.0042 0.0332 -0.008 0.0183 -0.0173 -0.0017 -0.0058
GE GS HD INTC IBM JPM JNJ MCD MRK MSFT
1/9/2012 0.0272 -0.007 0.0259 0.0379 -0.0253 0.0164 -0.0144 0.0081 0.0023 0.0362
NKE PFE PG TRV UTX UNH VZ V WMT DIS
1/9/2012 0.0159 -0.0068 -0.0028 0.0119 -0.0075 0.0239 -0.0217 -0.03 -0.0191 0.0376
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14. What is not a Time Series?
When time periods are not the same: For
example in a single time series both yearly and
quarterly data cannot be mixed
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15. Where do we encounter TS Data?
As our examples show, almost everywhere we
can come across time series data
Financial reports deal with TS data on a daily basis. To take a
decision on the bank rates, they have to look at the past data
and project it for the future
All manufacturers of multiple items have to optimize their
production process
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16. Where do we encounter TS Data?
Companies need to evaluate their manpower requirements
from historic data and take a decision regarding hiring
Portfolio managers try to understand stock movements based
on past data so that they can be more effective in advising
their clients how best to invest
Movement of the demand of electricity consumption pattern
provides policy makers impetus on where to build the new
electricity production plant and when
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17. Where do we encounter TS Data?
Based on the demands of airline tickets between cities, airlines
create their dynamic ticket pricing
Based on past data on booking pattern hotels decide on
whether any discounts are to be offered in room pricing at
certain times of the year
In short, time series data is being collected and utilized in all
data driven decision mechanisms
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18. What are the special features of TS
Data?
The most important feature that make TS analysis challenging
and none of the other machine learning techniques are
applicable is because
Data are not independent
One defining characteristic of time series is that this is a list of observations
where the ordering matters.
Ordering is very important because there is dependency and changing the order
will change the meaning of the data.
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19. If data is cross-sectional
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Make Year of Make MPG Horsepower Weight
AMC 80 24.3 90 3003
Audi 80 34.3 78 2188
Buick 81 22.4 110 3415
Chevy 82 27 90 2950
Chrysler 82 26 92 2585
Datsun 81 32.9 100 2615
Make Year of Make MPG Horsepower Weight
Datsun 81 32.9 100 2615
Audi 80 34.3 78 2188
Chevy 82 27 90 2950
Chrysler 82 26 92 2585
AMC 80 24.3 90 3003
Buick 81 22.4 110 3415
Order of the
Observations
Does not
matter
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20. If data is time series
20
Order of the
Observations is
All Important
Year Month No. of Pairs
2011 Jan 742
2011 Feb 741
2011 Mar 896
2011 Apr 951
2011 May 1030
2011 Jun 697
Year Month
No. of
Pairs
2011 Mar 896
2011 Jan 742
2011 May 1030
2011 Feb 741
2011 Apr 951
2011 Jun 697
WRONG
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21. Objective of this Module
To learn to forecast!
Not all series is equally easy or difficult to forecast. It depends on
How well the contributing factors are understood
How much data is available
We should not try to forecast if the historical data available is for a
short duration
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2015 Jul 1119
2015 Aug 783
2015 Sep 901
2015 Oct 1023
2015 Nov 1209
2015 Dec 1013
Will not provide reliable forecast for
Next 6 months
Might get a working forecast for the
Next 1 or 2 months
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22. Degrees of Difficulty
Easy to Forecast:
• GDP of a country
• Passenger volume for an established airlines
Because there are plethora of data available and the system is stable
Not so Easy! Currency Exchange Rate
• Limited understanding of market force
• Self-correcting market – forecast values affect the exchange rate
movement
Notoriously difficult to predict
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23. Forecast Range
Very long range forecasts do not work well!!
• Forecasts are done under the assumption that the market and other
conditions in future are very much like the present
• Not that there will be no change in the market
• But the change is gradual, not a drastic change
• A financial crash like 2008 US market will send all forecasts into a tizzy
• Events like Demonetization would throw the forecasts into disarray
Based on the amount of data availability, one should not try to forecast
more than a few periods ahead
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24. Scope of Module
• Only univariate TS is explored
• No attempt made to forecast more than one
interdependent TS
• The variable measured in TS is assumed to be
continuous and have fairly decent volume
• Intermittent demand TS is not considered
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25. Next Step
In the next lesson we explore time series more
closely and understand different components
through visual methods
Thank You
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