Since I have been teaching data analytics in India for the past month, I have been searching for an example of text analytics using R. Several months ago I saw a demonstration […]
Text Analytics: Forecast = Cloudy
Jeffrey Strickland
Jeffrey Strickland, Ph.D., is the Author of “Predictive Analytics Using R” and a Senior Analytics Scientist with Clarity Solution Group. He has performed predictive modeling, simulation and analysis for the Department of Defense, NASA, the Missile Defense Agency, and the Financial and Insurance Industries for over 20 years. Jeff is a Certified Modeling and Simulation professional (CMSP) and an Associate Systems Engineering Professional. He has published nearly 200 blogs on LinkedIn, is also a frequently invited guest speaker and the author of 20 books including:
•Operations Research using Open-Source Tools
•Discrete Event simulation using ExtendSim
•Crime Analysis and Mapping
•Missile Flight Simulation
•Mathematical Modeling of Warfare and Combat Phenomenon
•Predictive Modeling and Analytics
•Using Math to Defeat the Enemy
•Verification and Validation for Modeling and Simulation
•Simulation Conceptual Modeling
•System Engineering Process and Practices
•Weird Scientist: the Creators of Quantum Physics
•Albert Einstein: No one expected me to lay a golden eggs
•The Men of Manhattan: the Creators of the Nuclear Era
•Fundamentals of Combat Modeling
•LinkedIn Memoirs
•Quantum Phaith
•Dear Mister President
•Handbook of Handguns
•Knights of the Cross: The True Story of the Knights Templar
What Do Analytics Consultants Do? Part 1
I am often asked about the kinds of Analytics I perform as a consultant to address the questions my clients pose. The “real question” behind this is: What kind of Analytics do […]
Time Series – Interventions and Contribution
Introduction This article illustrates an analysis of the President George W. Bush’s job approval from January 2001 through Sep 2004 with disposable income excluded from the statistical model. Presidents with a job […]
What’s up with Gold?
A colleague recently asked me perform a time series analysis of gold prices and forecast future prices with the assumption that they would continue to decline. In this analysis with will not […]
Two General Aspects of Time Series Patterns
Introduction Though I have discussed other components of time series data, I can describe most time series patterns in terms of two basic classes of components: trend and seasonality. The first represents a […]
Moving averages
In a recent post “Components of Time Series Data“, I talked about decomposing a time series into its component parts and in “Exponential Smoothing of Times Series Data in R“, I talked about […]
Exponential Smoothing of Time Series Data in R
This article is not about smoothing ore into gems though your may find a few gems herein. Systematic Pattern and Random Noise In “Components of Time Series Data”, I discussed the components […]
Components of Time Series Data
In “What is Time Series Analysis?” I presented some basic concepts and uses for time series models, but I did not write much about time series data. Here we will explore characteristics […]
Unobserved Component Models in R
Introduction In my last Time Series article, “Components of Time Series Data,” I discussed the trend, seasonal and cyclical components of time series data. Here I will discuss an effective method for […]
Time Series Analysis using iPython
In this example, we will examine ARMA and ARIMA models with Python using the Statsmodels package. This package can be downloaded at http://statsmodels.sourceforge.net/stable/index.html. Autogressive Moving-Average Processes (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA) […]
BI Corner 