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Master’s in Time Series, Signals and Anomaly Detection

Online master's programme of 1,900 hours in time series, signals and anomaly detection: sensors, spectra, forecasting and root cause analysis. 12 modules covering digital signal processing, statistical and machine learning forecasting, anomaly and change detection, signal classification and real-time systems, with an assigned technical tutor.

Duration12–14 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Analyse signals and time series in the time and frequency domains.

02

Apply filtering and digital signal processing techniques.

03

Build statistical and machine learning models for forecasting.

04

Detect anomalies and change points in univariate and multivariate series.

05

Classify industrial and biomedical signals.

06

Implement real-time detection on streaming platforms.

07

Validate and deploy forecasting and alerting systems.

Profile

Who it's for

  • Time series data scientists looking to broaden their toolkit.
  • Signal processing engineers moving into machine learning.
  • Forecasting analysts.
  • Industrial monitoring engineers.
  • AIOps specialists.
  • Fraud analysts working with transactional data.
  • Engineering or science graduates aiming to enter industry.
Standards & tools

What you'll work with

  • ISO 7870-1 (control charts)
  • ISO 13374 (condition monitoring and diagnostics of machines – data processing)
  • ISO 20816-1 (measurement and evaluation of machine vibration)
  • IEC 61000-4-30 (power quality measurement methods)
  • ISO 8000 (data quality)
  • Regulation (EU) 2024/1689 (AI Act)
  • Python SciPy
  • statsmodels
  • sktime
  • InfluxDB
  • Apache Flink
Syllabus

12 modules

  1. 1.1Continuous and discrete signals
  2. 1.2Sampling and the Nyquist theorem
  3. 1.3Aliasing and anti-aliasing filtering
  4. 1.4Stationarity and ergodicity
  5. 1.5Trend, seasonality and noise
  6. 1.6Autocorrelation and spectrum
  7. 1.7Multivariate series
  8. 1.8Industrial sources of time series
  9. 1.9Typical acquisition errors
  10. 1.10Case study: exploratory analysis of compressor data
  1. 2.1Discrete Fourier transform
  2. 2.2FFT and spectral resolution
  3. 2.3Windows and spectral leakage
  4. 2.4FIR and IIR filters
  5. 2.5Filter design with SciPy
  6. 2.6Hilbert transform and envelope
  7. 2.7Wavelet transform
  8. 2.8Spectrograms and STFT
  9. 2.9Resampling and decimation
  10. 2.10Lab: filtering vibration signals
  1. 3.1ARIMA models
  2. 3.2SARIMA and seasonality
  3. 3.3Exponential smoothing and ETS
  4. 3.4VAR models
  5. 3.5State-space models
  6. 3.6Kalman filter
  7. 3.7GARCH models
  8. 3.8Residual diagnostics
  9. 3.9Selection by information criteria
  10. 3.10Practical exercise: consumption forecasting with SARIMA
  1. 4.1Lag features and windows
  2. 4.2Gradient boosting for time series
  3. 4.3Temporal validation and backtesting
  4. 4.4LSTM and GRU recurrent networks
  5. 4.5Temporal convolutional networks
  6. 4.6Transformers for time series
  7. 4.7Time series foundation models
  8. 4.8Probabilistic and quantile forecasting
  9. 4.9Hierarchical forecasting
  10. 4.10Lab: multivariate forecasting with LightGBM and LSTM
  1. 5.1Types: point, contextual and collective
  2. 5.2Statistical methods and statistical process control
  3. 5.3CUSUM change detection
  4. 5.4Isolation Forest and LOF
  5. 5.5Autoencoders and reconstruction
  6. 5.6Matrix profile
  7. 5.7Anomalies in multivariate series
  8. 5.8Thresholds and false alarms
  9. 5.9Anomaly benchmarks
  10. 5.10Practical exercise: anomaly detection with matrix profile
  1. 6.1Change point detection
  2. 6.2PELT and binary segmentation
  3. 6.3Online Bayesian methods
  4. 6.4Regime detection
  5. 6.5Events in process signals
  6. 6.6Drift detection
  7. 6.7DTW time alignment
  8. 6.8Detection of recurring patterns
  9. 6.9Evaluation with detection latency
  10. 6.10Lab: segmentation of machine cycles
  1. 7.1Time-domain feature extraction
  2. 7.2Spectral features
  3. 7.3ROCKET and MiniRocket
  4. 7.41D convolutional networks
  5. 7.5Industrial sound classification
  6. 7.6Electrocardiograms and biosignals
  7. 7.7Vibration and fault diagnosis
  8. 7.8Transfer learning
  9. 7.9Interpretability in classifiers
  10. 7.10Practical exercise: defect classification from sound
  1. 8.1Process monitoring
  2. 8.2In-line quality control
  3. 8.3Energy consumption and disaggregation
  4. 8.4Power grids and supply quality
  5. 8.5Structural sensors
  6. 8.6Fraud and non-technical losses
  7. 8.7Fleet telemetry
  8. 8.8Renewable generation
  9. 8.9Lessons from real cases
  10. 8.10Case study: smart alarm in a chemical plant
  1. 9.1System metrics and AIOps
  2. 9.2Logs as time series
  3. 9.3Network intrusion detection
  4. 9.4Network traffic and anomalies
  5. 9.5Transaction fraud detection
  6. 9.6Capacity and load forecasting
  7. 9.7Alert correlation
  8. 9.8SIEM integration
  9. 9.9Model operation
  10. 9.10Workshop: anomaly detector for server metrics
  1. 10.1Stream processing
  2. 10.2Kafka and Flink for time series
  3. 10.3Time series databases
  4. 10.4Online detection with windows
  5. 10.5Edge inference
  6. 10.6Latency and throughput
  7. 10.7Handling missing data live
  8. 10.8Alerts and notification
  9. 10.9Monitoring dashboards
  10. 10.10Practical exercise: online detector on Kafka
  1. 11.1Forecasting metrics: MAE, MAPE and CRPS
  2. 11.2Anomaly metrics with scarce labels
  3. 11.3Validation with domain experts
  4. 11.4Explaining alerts
  5. 11.5Retraining and drift
  6. 11.6MLOps for time series
  7. 11.7Documentation and traceability
  8. 11.8Applicable regulatory requirements
  9. 11.9Cost of error
  10. 11.10Case study: validation plan for an alerting system
  1. 12.1Problem and dataset
  2. 12.2Exploratory analysis
  3. 12.3Signal preprocessing
  4. 12.4Forecasting or detection models
  5. 12.5Comparative evaluation
  6. 12.6Streaming implementation
  7. 12.7Validation with users
  8. 12.8Conclusions
  9. 12.9Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so you can combine the programme with your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python SciPy, statsmodels, sktime, InfluxDB and Apache Flink, in free, academic or evaluation versions. Testing is studied on real procedures for backtesting forecasting models, anomaly detection with expert-labelled data and streaming detection tests. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO 7870-1, ISO 13374, ISO 20816-1, IEC 61000-4-30, ISO 8000, Regulation (EU) 2024/1689.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 7870-1, ISO 13374 and ISO 20816-1 applied to real cases, not as a theoretical annex.
Industry software:Python SciPy, statsmodels, sktime, InfluxDB and Apache Flink, in free, academic or evaluation versions.
Master's Thesis with defence:a complete forecasting or anomaly detection system, from exploratory analysis to streaming implementation and validation with users, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Signal preprocessing
Explore and filter a real industrial signal dataset, such as compressor vibrations.
Forecasting model
Build and backtest statistical and machine learning forecasting models.
Anomaly detector
Detect anomalies and change points with matrix profile and related methods.
Streaming deployment
Run the detector online on Kafka with alerts and dashboards.
Master's Thesis
Deliver a complete forecasting or detection system validated with users, defended before a panel.
Career opportunities

Where this takes you

Time series data scientistSignal processing engineerForecasting analystIndustrial monitoring engineerAIOps specialistFraud analyst
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer able to run the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Python SciPy, statsmodels, sktime, InfluxDB and Apache Flink, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent demonstrable professional experience), a computer able to run the software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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