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 […]

# Tag: machine learning

## 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. […]

## pseudo-harmonic FOREX prediction with machine learning (part one)

“Harmonic” trading methods seek patterns in the relationships between neighboring peaks and valleys in the time series. Particularly, harmonic traders seek pre-specified ratios in the price differences among a series of peaks and valleys. For example, a trader might observe the following pattern: Let A, B, C, D, and E be the points in the […]

## 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 […]

## Bayesian network modeling stock price change

Update 29 April 2018 I suspect this result is erroneous in that the graph often shows two arrows between any two given nodes, one inward and one outward. I’ll investigate this further and get back to you… – Emily Introduction Taking a cue from the systems biology folks, I decided to model stock price change […]

## overfitting in statistics and machine learning (part one)

Overfitting is a common risk when designing statistical and machine-learning models. Here I give a brief demonstration of overfitting in action, using simple regression models. A later post will more rigorously address how to quantify and avoid overfitting. We start by sampling data from the process using the R code: Then we produce a linear […]

## simulated ROC curves

How receiver operating characteristic (ROC) curves vary with simulated data having stepped degrees of separation: Computational Notes These were created in R using the “ROCR” package. Be sure to say “ROCR” really fast! The simulated data are normally distributed within each group.