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Hands On Monotonic Time Series Forecasting with XGBoost, using Python

This is how to use XGBoost in a forecasting scenario, from theory to practice

Piero Paialunga
TDS Archive
10 min readMar 29, 2024

Image made by author using DALL·E-3

A couple of months ago, I was on a research project and I had a problem to solve involving time series.

The problem was fairly straightforward:

“Starting from this time series with t timesteps, predict the next k values

For the Machine Learning enthusiasts out there, this is like writing “Hello World”, as this problem is extremely well known to the community with the name “forecasting”.

The Machine Learning community developed many techniques that can be used to predict the next values of a timeseries. Some traditional methods involve algorithms like ARIMA/SARIMA or Fourier Transform analysis, and other more complex algorithms are the Convolutional/Recurrent Neural Networks or the super famous “Transformer” one (the T in ChatGPT stands for transformers).

While the problem of forecasting is a very well-known one, it is maybe less rare to address the problem of forecasting with constraints.
Let me explain what I mean.

You have a time series with a set of parameters X and the time step t.
The standard time forecasting…

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TDS Archive
TDS Archive

Published in TDS Archive

An archive of data science, data analytics, data engineering, machine learning, and artificial intelligence writing from the former Towards Data Science Medium publication.

Piero Paialunga
Piero Paialunga

Written by Piero Paialunga

PhD in Aerospace Engineering at the University of Cincinnati. Machine Learning Engineer @ Gen Nine, Martial Artist, Coffee Drinker, from Italy.

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