Machine Learning for Time Series (Master MVA)
Teaching material and outline of the course Machine Learning for Time Series (Master MVA) during 20202021.
Course description
In many application contexts (health, economy, advertising...), the data collected takes the form of time series. The fundamental challenge then consists in choosing an adapted representation, allowing to take into account the temporal information as well as possible.
Machine Learning for time series gathers a large number of unsupervised or supervised tasks such as prediction, classification, completion/interpolation, query by content/indexation, clustering, segmentation/changepoint detection or anomaly detection. But in reality, most of work for a data scientist dealing with temporal data consists in a series of hidden tasks such as:
 Understand the data: know where they come from, how they were acquired, what are their characteristics, interact with domainexperts and understand their problems
 Improve the data: find accurate representation spaces where the events of interest can be seen, consolidate the data (denoising, detrending, detection/removal of outliers)
 Model the data: physical/statistical or expertbased models, simple, adaptive and interpretable models
 Extract information from the data: find repetitive patterns, features of interest, changepoints
This course aims to provide an overview of ML techniques to study time series, in different tasks such as pattern extraction and recognition, anomaly detection, prediction, interpolation etc. The course will mostly focus on these often poorlydocumented hidden tasks and introduce several recent ML methods that will help the future data scientist to mine, but above all to understand time series. The course will be widely illustrated on real data and problems from current challenges and will emphasize aspects related to data understanding and interpretation. Note that in its current form, the course will only briefly discuss Deep Learning algorithms.
Presentation slides are available here
Outline and planning
Lectures and tutorial sessions will take place every Friday (afternoon or morning) at ENS Paris Saclay in Room 1Z33 (except for the oral presentations). For the tutorial sessions, students are asked to bring their personal laptops.
ENS ParisSaclay is closed for students until February 2021: until further notice, all sessions will be on Zoom
15/01/2021 14:00 → 17:00 Zoom 
Introduction


Lecture 1: Pattern Recognition and Detection
 Problem statement
 Comparing time series
 Euclidean distance
 Normalized Euclidean distance
 Dynamic Time Warping (DTW)
 Detecting patterns in time series
 Euclidean distance
 DTW
 Learning patterns from time series
 Distancebased pattern extraction
 Dictionarybased pattern extraction


22/01/2021 09:00 → 12:00 Zoom 
Lecture 2: Feature Extraction and Selection
 Problem statement
 Feature extraction
 Spectral features
 Statistical features
 Deep Learning features
 Other features
 Feature selection
 Unsupervised setting
 Supervised setting


29/01/2021 14:00 → 17:00 Zoom 
Tutorial 1 on Lectures 1 & 2
 N/A

05/02/2021 14:00 → 17:00 Zoom 
Lecture 3: Models and Representation Learning
 Problem statement
 Standard models
 Sinusoidal model
 Trend+Seasonality model
 AR models (and variants)
 Hidden Markov model
 Representation learning
 Standard representations
 Notion of sparsity
 Sparse coding
 Dictionary learning


12/02/2021 14:00 → 17:00 Zoom 
Lecture 4: Denoising, Detrending, Interpolation and Outlier Removal
 Problem statement
 Denoising
 Filtering
 Sparse approximations
 Lowrank approximations
 Other techniques
 Detrending
 LeastSquare regression
 Other techniques
 Interpolation of missing samples
 Polynomial interpolation
 Lowrank interpolation
 Modelbased interpolation
 Outlier removal
 Isolated samples
 Contiguous samples


19/02/2021 14:00 → 17:00 Zoom 
Tutorial 2 on Lectures 3 & 4
 N/A

26/02/2021 14:00 → 17:00 Zoom 
Lecture 5: ChangePoint and Anomaly Detection
 Problem statement
 Change point detection
 Dealing with nonstationary time series
 Problem statement
 Cost functions
 Search methods
 Finding the number of change points
 Anomaly detection
 Outlier detection
 Statistical methods
 Modelbased methods
 Distancebased methods


05/03/2021 14:00 → 17:00 Zoom 
Lecture 6: Multivariate Time Series
 Problem statement
 Models for multivariate time series
 Vector autoregressive models
 Multivariate dictionary learning
 Graph signal processing
 Concepts and definitions
 Graph Fourier Transform (GFT)
 Bandlimitedness and smoothness
 Graph filtering
 Graph learning


12/03/2021 14:00 → 17:00 Zoom 
Tutorial 3 on Lectures 5 & 6
 N/A

26/03/2021 14:00 → 17:00 Room 1X10 
Oral presentations


Validation
 Tutorials (25%): commented Jupyter notebook due at the end of each tutorial session
 Miniproject (75%):
 Report (25%): PDF file, 5 pages
 Source code (25%): commented Jupyter notebook
 Oral presentation (25%): 10 min presentation
Additional ressources
Useful references


List of possible topics/projects

