Course Description
Time-series data — sensor readings, process parameters, energy consumption, production rates — is the most common form of industrial data. This course develops the specialised methods required to analyse, model, and forecast industrial time-series, from classical statistical approaches to modern machine learning.
Participants learn to handle the characteristics of industrial time-series (trend, seasonality, noise, irregular sampling), apply forecasting methods such as ARIMA and exponential smoothing, and use machine learning and deep learning for sensor forecasting. The course is directly applicable to demand forecasting, equipment wear prediction, and sensor trending.
What you will achieve
By the end of this training course, participants will be able to:
- Understand the characteristics of industrial time-series data
- Explore and decompose time-series (trend, seasonality, noise)
- Prepare and clean time-series sensor data
- Apply classical forecasting: ARIMA and exponential smoothing
- Use machine learning for time-series forecasting
- Apply deep learning approaches (LSTM) to sensor data
- Evaluate forecasting accuracy
- Apply forecasting to industrial problems (demand, wear, load)
How the course is delivered
The course combines time-series theory with practical forecasting exercises on industrial data. Participants work through decomposition, classical models, and machine learning approaches applied to sensor and operational time-series.
The programme balances statistical rigour with practical forecasting application across industrial use cases.
Designed for
This training course is ideal for:
- Industrial data scientists and analysts
- Reliability and process engineers
- Planning and operations professionals
- Engineers forecasting equipment and demand
- Technical staff working with sensor data
Daily programme
- Characteristics of industrial time-series
- Trend, seasonality, and noise
- Decomposition and exploration
- Preparing time-series data
- Stationarity and differencing
- ARIMA models
- Exponential smoothing
- Model selection and diagnostics
- Feature-based forecasting
- Machine learning models for time-series
- Deep learning: LSTM and sequence models
- Multi-variate forecasting
- Forecasting accuracy and evaluation
- Demand and load forecasting
- Equipment wear and degradation trending
- Case study workshop and review
Certification & accreditation
BII Certificate of Completion
BII Certificate of Completion Upon successful completion, participants receive a BII Development Institute Certificate of Completion with a unique reference code that is independently verifiable. Our certificates are recognised internationally and reflect successful completion of your chosen programme.
