Armando B. Mendes

Time Series Analysis using R-Forecast package

Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional

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Demonstra algumas das funcionalidades do pacote R forecast

In today’s blog post, we shall look into time series analysis using R package – forecast. Objective of the post will be explaining the different methods available in forecast package which can be applied while dealing with time series analysis/forecasting.

Tags: data mining, previsão, R-software

Read more | Comments off | January 31st, 2018

Avoiding a common mistake with time series

Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional, materiais para profissionais

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Um caso em q a tendência mascara o resto da série criando correlações elevadas

A basic mantra in statistics and data science is correlation is not causation, meaning that just because two things appear to be related to each other doesn’t mean that one causes the other. This is a lesson worth learning.

If you work with data, throughout your career you’ll probably have to re-learn it several times. But you often see the principle demonstrated with a graph like this:

Dow Jones vs. Jennifer Lawrence

One line is something like a stock market index, and the other is an (almost certainly) unrelated time series like “Number of times Jennifer Lawrence is mentioned in the media.” The lines look amusingly similar. There is usually a statement like: “Correlation = 0.86”. Recall that a correlation coefficient is between +1 (a perfect linear relationship) and -1 (perfectly inversely related), with zero meaning no linear relationship at all. 0.86 is a high value, demonstrating that the statistical relationship of the two time series is strong.

The correlation passes a statistical test. This is a great example of mistaking correlation for causality, right? Well, no, not really: it’s actually a time series problem analyzed poorly, and a mistake that could have been avoided. You never should have seen this correlation in the first place.

The more basic problem is that the author is comparing two trended time series. The rest of this post will explain what that means, why it’s bad, and how you can avoid it fairly simply. If any of your data involves samples taken over time, and you’re exploring relationships between the series, you’ll want to read on.

Tags: previsão

Read more | Comments off | January 31st, 2018

How and Why: Decorrelate Time Series

Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional, materiais para profissionais

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O problemas das autocorrelações nas séries cronológicas.

When dealing with time series, the first step consists in isolating trends and periodicites. Once this is done, we are left with a normalized time series, and studying the auto-correlation structure is the next step, called model fitting. The purpose is to check whether the underlying data follows some well known stochastic process with a similar auto-correlation structure, such as ARMA processes, using tools such as Box and Jenkins. Once a fit with a specific model is found, model parameters can be estimated and used to make predictions.

A deeper investigation consists in isolating the auto-correlations to see whether the remaining values, once decorrelated, behave like white noise, or not. If departure from white noise is found (using a few tests of randomness), then it means that the time series in question exhibits unusual patterns not explained by trends, seasonality or auto correlations. This can be useful knowledge in some contexts such as high frequency trading, random number generation, cryptography or cyber-security. The analysis of decorrelated residuals can also help identify change points and instances of slope changes in time series, or reveal otherwise undetected outliers.

Tags: previsão

Read more | Comments off | January 31st, 2018

Time Series Forecasting and Internet of Things (IoT) in Grain Storage

Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional

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Aplicações reais de previsão com séries cronológicas

Grain storage operators are always trying to minimize the cost of their supply chain. Understanding relationship between receival, outturn, within storage site and between storage site movements can provide us insights that can be useful in planning for the next harvest reason, estimating the throughput capacity of the system, relationship between throughout and inventory. This article explores the potential of scanner data in advance analytics. Combination of these two fields has the potential to be useful for grain storage business. The study describes Grain storage scenarios in the Australian context.

Tags: previsão

Read more | Comments off | January 31st, 2018

Data Analysis Method: Mathematics Optimization to Build Decision Making

Posted by Armando Brito Mendes | Filed under Investigação Operacional, matemática, SAD - DSS

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Uma pequena introdução à utilização de otimização na análise de dados

Optimization is a problem associated with the best decision that is effective and efficient decisions whether it is worth maximum or minimum by way of determining a satisfactory solution.

Optimization is not a new science. It has grown even since Newton in the 17th century discovered how to count roots. Currently the science of optimization is still evolving in terms of techniques and applications. Many cases or problems in everyday life that involve optimization to solve them. Lately much developed especially in the emergence of new techniques to solve the problem of optimization. To mention some, among others, conic programming, semi definite programming, semi infinite programming and some meta heuristic techniques.

Tags: análise de dados, data mining, otimização

Read more | Comments off | January 3rd, 2018

Statistical Associates E-Book Catalog

Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional, matemática, materiais ensino

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e-books grátis.

TITLE	INFO	DESCRIPTION	EDITION	FREE	KINDLE
NO PASSWORD REQUIRED FOR TITLES IN THIS SECTION
2013 Annual Report, Statistical Associates Publishers	Info	Pages: 8. Coverage: General.	2013	Free	No Kindle edition
10 Worst Statistical Mistakes and Pitfalls	Info	Coverage: For selected statistical procedures	2015	Free	No Kindle edition
Creating Simulated Datasets	Info	Pages: 15. Coverage: General, SPSS.	2012	Free	No Kindle edition
Game Theory	Info	Pages: 15. Coverage: General.	2012	Free	No Kindle edition
Probability	Info	Pages: 15. Coverage: General, SPSS, SAS, Stata.	2013	Free	No Kindle edition
Testing Statistical Assumptions	Info	Pages: 51. Coverage: General, SPSS.	2012	Free	Coming
E-MONOGRAPHS: ALL $5 AT AMAZON/KINDLE
Association, Measures of	Info	Pages: 49. Coverage: General, SPSS.	2012	Free	Buy at Amazon
Correlation	Info	Pages: 60. Coverage: General, SPSS, SAS, Stata.	2013	Free	Buy at Amazon
Correspondence Analysis	Info	Pages: 37. Coverage: General, SPSS.	2012	Free	Buy at Amazon
Crosstabulation	Info	Pages: 60. Coverage: General, SPSS, SAS, Stata.	2013	Free	Buy at Amazon
Curve Fitting & Nonlinear Regression	Info	Pages: 53. Coverage: General, SPSS.	2012	Free	Buy at Amazon
Discriminant Function Analysis	Info	Pages: 52. Coverage: General, SPSS.	2012	Free	Buy at Amazon
Life Tables & Kaplan-Meier Analysis	Info	Pages: 32. Coverage: General, SPSS.	2012	Free	Buy at Amazon
Literature Review in Research and Dissertation Writing	Info	Pages: 52. Coverage: General.	2013	Free	Buy at Amazon
Multidimensional Scaling	Info	Pages: 55. Coverage: General, SPSS.	2012	Free	Buy at Amazon
Network Analysis	Info	Pages: 35. Coverage: General, UCINET.	2012	Free	Buy at Amazon
Ordinal Regression	Info	Pages: 93. Coverage: General, SPSS, SAS, Stata.	2014	Free	Buy at Amazon
Parametric Survival Analysis (Event History Analysis)	Info	Pages: 64. Coverage: General, Stata, SAS.	2012	Free	Buy at Amazon
Partial Correlation	Info	Pages: 40. Coverage: General, SPSS, SAS, Stata.	2014	Free	Buy at Amazon
Path Analysis	Info	Pages: 81. Coverage: General, SPSS AMOS. SAS, Stata.	2014	Free	Buy at Amazon
Power Analysis	Info	Pages: 36. Coverage: General, SPSS SamplePower, G*Power.	2012	Free	Buy at Amazon
Probit Regression & Response Models	Info	Pages: 92. Coverage: General, SPSS.	2012	Free	Buy at Amazon
Research Design	Info	Pages: 53. Coverage: General.	2013	Free	Buy at Amazon
Scales and Measures	Info	Pages: 91. Coverage: General, SPSS, SAS, Stata, WINSTEPS, jMetric	2013	Free	Buy at Amazon
Survey Research & Sampling	Info	Pages: 82. Coverage: General.	2013	Free	Buy at Amazon
Two-Stage Least Squares Regression	Info	Pages: 45. Coverage: General, Stata, SPSS, SAS.	2013	Free	Buy at Amazon
Variance Components Analysis	Info	Pages: 37. Coverage: General, SPSS, SAS.	2012	Free	Buy at Amazon
WLS: Weighted Least Squares Regression	Info	Pages: 54. Coverage: General, SPSS, SAS, Stata.	2013	Free

Tags: data mining, motores de busca

Read more | Comments off | May 29th, 2015

Markov Chains explained visually

Posted by Armando Brito Mendes | Filed under Investigação Operacional, matemática, materiais ensino, visualização

Boa forma de perceber como funcionam as cadeias de Markov

Adding on to their series of graphics to explain statistical concepts, Victor Powell and Lewis Lehe use a set of interactives to describe Markov Chains. Even if you already know what Markov Chains are or use them regularly, you can use the full-screen version to enter your own set of transition probabilities. Then let the simulation run.

Tags: grafos, otimização

Read more | Comments off | August 12th, 2014