Machine Learning for Time Series (Master MVA)
Teaching material and outline of the course Machine Learning for Time Series (Master MVA) during 2020-2021.
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/change-point 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 domain-experts 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 expert-based models, simple, adaptive and interpretable models
- Extract information from the data: find repetitive patterns, features of interest, change-points
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 poorly-documented 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 Paris-Saclay 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
- Distance-based pattern extraction
- Dictionary-based 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
- Low-rank approximations
- Other techniques
- Detrending
- Least-Square regression
- Other techniques
- Interpolation of missing samples
- Polynomial interpolation
- Low-rank interpolation
- Model-based 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: Change-Point and Anomaly Detection
- Problem statement
- Change point detection
- Dealing with non-stationary time series
- Problem statement
- Cost functions
- Search methods
- Finding the number of change points
- Anomaly detection
- Outlier detection
- Statistical methods
- Model-based methods
- Distance-based 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
- Mini-project (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
|
|