Forecasts can be very useful for counteracting intuition and biases, thus leading to more accurate and effective decisions. Forecasts are increasingly a necessity in organizations operating in a wide range of fields, from finance to consumption analysis or biological signals.

The Data Science and Time Series Forecasting course introduces the task of forecasting, from the methods for creating classic models to the training of dedicated neural networks. In addition to introducing the essential techniques for describing and transforming them, simple methods for classifying and discovering motifs are also covered.

Operating Hours:

Start Date: March 2, 2026
End Date: April 1, 2026
Schedule: Mondays and Wednesdays, 5:30 PM to 8:00 PM
Price: €1,080
Format: Remote
Duration: 25 hours

More information and applications. 

Objectives:
– Characterise time series, recognising the most common temporal distortions and being able to deal with them;
– Apply transformations in the same space, and to different spaces; – Develop classical forecasting models and those based on specialized neural networks and evaluate the information discovered.

Target audience:
This course is intended for all licensed professionals with knowledge of probability, statistics, and basic programming, especially those interested in working with data subject to fluctuations, such as seasonality, changes in demand, competitor movements, strikes, and economic cycles.

Training Methodology:
As an engineering program, the course unfolds through theoretical and practical sessions, during which a project will be developed. This consists on applying and documenting the process in the context of a specific problem, exploring all the techniques covered and studying the impact of their use on the quality of the models developed.

Structure
Summary of Program Content

1 – Time Series and Forecasting;
2 – Time Series Transformation;
3 – Simple Models;
4 – Regressive Models;
5 – Neural Networks;
6 – Recurrent Neural Networks;
7 – Project Support;
8 – Predictors;
9 – Implementation;
10 – Evaluation.