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Diploma in Time Series Models for Engineering (ARIMA→DL)

About us Diploma in Time Series Models for Engineering (ARIMA→DL)

The Diploma in Time Series Models for Engineering (ARIMA→DL) focuses on the predictive analysis of sequential data, integrating ARIMA modeling and deep learning (DL) techniques. It applies these methodologies in various engineering fields, such as demand forecasting, process control, and signal analysis. Tools and software for model development are explored, covering everything from data preparation to accuracy evaluation. It enables participants to understand and apply advanced time series models to solve engineering problems.

The diploma provides a solid foundation in statistics and machine learning, necessary for the analysis of time series data. It focuses on the practical implementation of models, offering experience in the use of Python libraries and frameworks. The goal is to train professionals to analyze, model, and predict the behavior of dynamic systems over time, optimizing decision-making in various industrial sectors.

Target keywords (natural in the text): time series, ARIMA modeling, deep learning, prediction, process control, data analysis, Python, statistics, machine learning, engineering.

Diploma in Time Series Models for Engineering (ARIMA→DL)
  • Format: Online
  • Duration: 8 months
  • Hours: 900 H
  • Language: ES / EN
  • Credits: 60 ECTS
  • Registration date: 04-07-2026
  • Strat date: 14-08-2026
  • Available places: 4

1.795 $

Competencias y resultados

Qué aprenderás

1. Time Series Mastery: ARIMA to Deep Learning for Engineering

Para quien va dirigido nuestro:

Diploma in Time Series Models for Engineering (ARIMA→DL)

9.9 Introduction to Time Series: Definition and Components
9.9 Stationarity and Transformations
9.3 Autocorrelation and Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF)
9.4 ARIMA Models: Components and Notation
9.5 Identifying the ARIMA Model: Selecting Order p, d, q
9.6 Estimating ARIMA Parameters
9.7 Diagnosing the ARIMA Model: Residuals and Testing
9.8 Prediction with ARIMA Models
9.9 Practical Implementation: Analysis and Prediction with Software
9.90 Limitations and Alternatives to ARIMA

9.90

Proyectos tipo capstones

  • Naval Fleet Forecasting: ARIMA and DL for resource optimization and strategic planning.
  • Predictive Maintenance: Failure analysis of naval equipment using time series.
  • Demand Forecasting: Time series modeling for efficient supply and logistics management.
  • Maritime Safety: Incident prediction through historical data analysis.

Admisiones, tasas y becas

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