Time Series: why do we need Stationarity and Ergodicity

A time series is a series of data points indexed in time order, normally with equally spaced points in time. Examples of time series are stocks’ prices, monthly returns, company’s sales and so forth. Time series can be seen as data with a target variable (price, returns, amount of sales…) and one feature only: time. […]

Understanding Rejection Sampling method

Rejection sampling is a computational technique whose aim is generating random numbers from a target probability distribution f(x). It is related to the general field of MonteCarlo methods, whose core is generating repeated random sampling to make numerical estimation of unknown parameters. Some words about Randomness One might ask why a random variable with probability […]

Mapping and building machine learning algorithms on geodata with R

Sometimes the very representation method of data, by itself, can provide a huge amount of information and might direct you towards a good analysis. In this article, I will dwell on some interesting plotting methods, provided by R, which are pivotal if you are facing geodata. I will use the famous NYC Taxi Dataset, which […]

How to set and deploy your machine learning experiment with R

The aim of this article is providing a foretaste of the potentiality of machine learning algorithms using R, following step-by-step a standard procedure that, once got familiar, could be a good starting point to design customized models. The idea behind each model, indeed, is the same. In a nutshell, it consists of finding an algorithm […]