## FOREX correlation and causality

Consider the seven major currency pairs, sampled hourly over the last six months. We calculate the pairwise Pearson correlation coefficients to determine the degree with which each pair “moves” together: Values near one or negative one indicate high correlation, values with lower absolute value less so. Positive values indicate movement in the same direction; negative […]

## women’s style recommendation with artificial intelligence (part #2)

In “women’s style recommendation with artificial intelligence (part #1)”, I introduced my work toward developing artificial intelligence (AI) for fashion and style recommendation. Essentially, its an expert system built on a Bayesian belief network. Now I discuss model validation and next steps in the design iteration process. I first wanted to see if the trained […]

## women’s style recommendation with artificial intelligence (part #1)

Introduction We know several basic style “rules” (ha!) based on body shape: Skirts: “Apple” Body Shape: IF body shape is apple AND skirt has front zipper THEN don’t wear IF body shape is apple AND skirt has side zipper THEN wear IF body shape is apple AND skirt has no zipper THEN wear “Rectangular” Body […]

## machine learning in FOREX (part one: establishing a performance baseline)

Introduction We’ve been applying machine learning to FOREX price prediction. The performance of our models varies widely, so to establish a baseline we created a simple linear regression model with which we can compare performance of more sophisticated models against. What We Are Trying To Do Given a time-series of 26 four-hour price samples, we […]

## autocorrelation in FOREX

To inform the construction of a machine learning-based price prediction algorithm, we want to understand how many lags prove statistically significant with regard to autocorrelation in the seven major FOREX pairs. So we first choose 10,000 random time points between January 1, 2000 and January 1, 2017 for each of the seven pairs. Then we […]

## artificial intelligence in fashion (part one: brainstorming)

Brainstorming as usual: Fashion dictums involve many IF-THEN-ELSE rules. One can convert this into a decision engine (inference engine). User specifies their body shape, and a recommendation engine selects suitable clothing for them, taking into account the user’s tastes. Upload an image of a dress you want to buy, and specify the dress’s given size. […]

## Bayesian method for filtering out mRNA turnover rate bias from siRNA knockdown measurements

Abstract siRNA performance prediction calculations for a given siRNA may be divided into two broad categories: functions of the siRNA’s sequence, hereafter referred to as “intrinsic” properties of the siRNA, and functions of the target mRNA, hereafter referred to as “extrinsic” properties of the siRNA. When training a statistical or machine learning model to select […]

## DIY Twitter analytics (part 2: correlations)

I’ve been working with the Twitter API to develop my own Twitter analytics tool chain, and have been documenting the results on this blog. My last post on the subject described clustering my followers by their hashtag use to see whose tweets are most like mine. My goal of this project is to figure out best […]

## HRC Corporate Equality Index correlates with Fortune’s 50 most admired companies

The Human Right’s Campaign, one of America’s largest civil rights groups, scores companies in its yearly Corporate Equality Index (CEI) according to their treatment of lesbian, gay, bisexual, and transgender employees [1]. The companies automatically evaluated are the Fortune 1000 and American Lawyer’s top 200. Additionally, any sufficiently large private sector organization can request inclusion […]

## clustering stocks by price correlation (part 2)

In my last post, “clustering stocks by price correlation (part 1)“, I performed hierarchical clustering of NYSE stocks by correlation in weekly closing price. I expected the stocks to cluster by industry, and found that they did not. I proposed several explanations for this observation, including that perhaps I chose a poor distance metric for […]