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:

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
pdf
Lecture 1: Pattern Recognition and Detection
  1. Problem statement
  2. Comparing time series
    1. Euclidean distance
    2. Normalized Euclidean distance
    3. Dynamic Time Warping (DTW)
  3. Detecting patterns in time series
    1. Euclidean distance
    2. DTW
  4. Learning patterns from time series
    1. Distance-based pattern extraction
    2. Dictionary-based pattern extraction
pdf
22/01/2021
09:00 → 12:00
Zoom
Lecture 2: Feature Extraction and Selection
  1. Problem statement
  2. Feature extraction
    1. Spectral features
    2. Statistical features
    3. Deep Learning features
    4. Other features
  3. Feature selection
    1. Unsupervised setting
    2. Supervised setting
pdf
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
  1. Problem statement
  2. Standard models
    1. Sinusoidal model
    2. Trend+Seasonality model
    3. AR models (and variants)
    4. Hidden Markov model
  3. Representation learning
    1. Standard representations
    2. Notion of sparsity
    3. Sparse coding
    4. Dictionary learning
pdf
12/02/2021
14:00 → 17:00
Zoom
Lecture 4: Denoising, Detrending, Interpolation and Outlier Removal
  1. Problem statement
  2. Denoising
    1. Filtering
    2. Sparse approximations
    3. Low-rank approximations
    4. Other techniques
  3. Detrending
    1. Least-Square regression
    2. Other techniques
  4. Interpolation of missing samples
    1. Polynomial interpolation
    2. Low-rank interpolation
    3. Model-based interpolation
  5. Outlier removal
    1. Isolated samples
    2. Contiguous samples
pdf
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
  1. Problem statement
  2. Change point detection
    1. Dealing with non-stationary time series
    2. Problem statement
    3. Cost functions
    4. Search methods
    5. Finding the number of change points
  3. Anomaly detection
    1. Outlier detection
    2. Statistical methods
    3. Model-based methods
    4. Distance-based methods
pdf
05/03/2021
14:00 → 17:00
Zoom
Lecture 6: Multivariate Time Series
  1. Problem statement
  2. Models for multivariate time series
    1. Vector autoregressive models
    2. Multivariate dictionary learning
  3. Graph signal processing
    1. Concepts and definitions
    2. Graph Fourier Transform (GFT)
    3. Bandlimitedness and smoothness
    4. Graph filtering
    5. Graph learning
pdf
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

Additional ressources

Useful references
pdf
List of possible topics/projects pdf