Is Time Series Clustering Meaningless? (lots of dplyr)
Regardless of your interest in time series clustering, you might enjoy the dplyr and piping that I used to generate the results. Also, I have not seen dplyr do applied to autocorrelation ACF, so you might want to check that out in the last snippet of code.
Brand and Product Category Representation: Precursors to Preference Construction
Within the framework of utility theory and conjoint analysis, R provides both an introduction (Stated Preference Methods using R) and access to advanced algorithms (hierarchical Bayes choice modeling). However, generalization remains a problem. The experimental procedures that elicit stated preference are not the same as those in the marketplace where purchases are made for differing occasions, purposes and participants. Preferences are not well-formed and stable, but constructed on the fly within the choice context. Even price sensitivity depends on framing, which is why we see such robust and resistant order-effects when costs are increasing versus decreasing (e.g., a 10% price increase seems less objectionable when it comes after a proposed 15% increment than when it comes after a 5% raise).
Topic Modeling in 9/11 News Articles
This post describes a project to visualize topics in news articles related to the September 11th attacks and their lasting effects and consequences. I describe my motivation, the technical details of my implementation, and my reflections on some of the results.
The Value of Data, Part 1: Using Data as a Competitive Advantage
Here are some ways to apply data to create more defensible products:
• Data helps you provide great content recommendations.
• Data helps you provide more accurate ad targeting.
• Data helps you optimize pricing and offer pricing transparency.
• Data helps you provide a definitive destination for some type of content.
• Data helps you provide actionable insights.
• Data helps you make operations more efficient.
• Data helps you provide more accurate models and predictions.
• Data helps you improve categorization/tagging/sentiment analysis.
• Data helps you improve language parsing/semantic analysis.
• Data helps you create better AIs.
Granger Causality & Seasonal Adjustment
One decision that we often have to make when modelling with time-series data is whether to use “seasonally adjusted” data, or the original (unadjusted) data. In some cases the decision is effectively made for us – only the seasonally adjusted data are published. This arises, for example, with some U.S. macroeconomic data, and it can be a bit of a pain.