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Uncovering patterns and forecasting with time series data
Submitted by Pranav Modi (@pranavmodi) on Tuesday, 30 April 2013
Section: Analytics and Visualization Technical level: Intermediate
Understand time series analysis and its applications in industry and science. Uncover patterns in data - trends, seasonality, cyclical behavior.
Learn intuitive visualization techniques. Methods for noise reduction, clustering of time series using shape analysis.
Catch the R 'forecast' package in action.
Description : A time series is a sequence of observations which are ordered in time (or space). Examples of time series data include -
Business data - demand data, sales, inventory management.
Neuroscience data - EEG, EKG
Financial data - stock prices, currencies, derivatives
Climate data - tide levels, sunspots.
You will learn how to approach time series analysis, extract patterns and make predictions that have a huge impact.
Not a prerequisite, but exposure to R will help.
I work as a data scientist at a large consulting firm where we are frequently consulted on time series forecasting problems. This talk is distilled out of my experiments with time series analysis and learnings so far.
I am a functional programming enthusiast who has ventured into machine learning and data analysis. At my previous company Runa I worked on machine learning while hacking lisp! I'd be happy to share my experiences with Clojure and self-learning adventures in data analysis as well.