How to model time-to-event and ‘time between events’ on real data using the Exponential Family to optimize returns of time driven investments.

Intro

Usually, when we have time as a variable on a dataset, we can model other variables reflected in time itself, whether we apply Time Series Analysis or just use time to cut our data and apply continual ML or State Space Linear Models.

The meaningful features your model is missing maybe a one-liner away.

1. Theory

When we define a statistical model focused on a dependent variable, we attempt to explain the relationship between that dependent variable and other independent variable(s).

To compute these models, we need to vectorize our variables and align them in a way that every column is a variable (discretized) and every…

Boosting Performance by Generating Features from External Data with Python.

Intro

In this article, we’ll create a forecasting model to predict housing prices in Seattle. We will first make a model using the properties’ attributes such as sqft, rooms, bedrooms, bathrooms, view, etc.

Then we’ll significantly improve that model by generating features from external data such as proximity of cultural spaces…

Simplicity is key

Simplicity is the keynote of all true elegance.

-Abraham Maslow-

*Disclaimer: I am assuming that whoever has the ability to comprehend and execute the content of this article is savvy enough to perform a robust back-testing in every corner of their trading pipeline before actually running it in production. However…

Simplicity is key.

Goal

In this tutorial we’ll make a Machine Learning Pipeline that inputs News and applies NLP to generate predictions for Amazon Stock Price re-training through time.

We’ll also measure how profitable it would be in real life.

What we’ll do

  • Step 1: Set up technical prerequisites
  • Step 2: Get the data

A step-by-step tutorial using Python.

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On today’s harsh global…

Federico Riveroll

An attempt to separate signal from noise. | MSDS

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