Learning Time Series with Interventions from edX

If you have specific questions about this course, please contact us atsds-mm@mit.edu.

A time series is a time-stamped set of noisy observations from an underlying process that evolves over time. These observations are dependent on each other in a particular, unknown, fashion. Examples of such series include stock values, value of a currency with respect to the dollar, mean housing prices, the number of Covid-19 infections, or the pitch angle of an airplane during flights. Modeling such processes for the purpose of prediction or intervention is a fundamental problem in statistical learning.

This graduate-level course that will address three lines of development:

Learning Structured Models: In this module, we focus on learning the underlying stochastic dynamic model that generates the data. We discuss how algorithms depend on the underlying class of models adopted for this learning. We address the accuracy and reliability of our learned models.

Prediction: In this module, we make no assumptions on how the data is generated and focus on predicting the next outcome of the process based on past observations. In this context, we analyze Matrix and Tensor Completion Methods in providing such predictions and we analyze the accuracy of these prediction in the presence of noise, missing data.

Optimal Intervention and Reinforcement Learning (RL): A key ingredient of RL is a simulator that can estimate the value of a reward for a given intervention. In this module course, we build on techniques from RL as well as the first two parts to show how new intervention/control can be derived with better outcomes.

This course will consist of three hands-on projects, in which learners will apply knowledge gained in lectures, build models and implement algorithms to solve problems posed on real time series data sets.

This course is part of theMITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visithttps://micromasters.mit.edu/ds/.

What's inside

Learning objectives

Analyze time series through the perspective of linear time-invariant (lti) systems and use methods and tools such as spectral analysis.
Model time series using autoregressive moving average (arma) and integrated processes.

Perform prediction, imputation on general time series data using matrix completion methods.
Use various dynamical programming and reinforcement learning algorithms to optimize control and interventions for time series.

Analyze time series through the perspective of linear time-invariant (lti) systems and use methods and tools such as spectral analysis.
Model time series using autoregressive moving average (arma) and integrated processes.
Perform prediction, imputation on general time series data using matrix completion methods.
Use various dynamical programming and reinforcement learning algorithms to optimize control and interventions for time series.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers structured models, prediction, and optimal intervention, which are essential for advanced work in time series analysis and reinforcement learning

Includes hands-on projects using real time series datasets, allowing learners to apply theoretical knowledge to practical problems

Explores matrix and tensor completion methods for prediction, which are valuable techniques for handling noise and missing data in time series

Examines autoregressive moving average (ARMA) models, which are foundational for understanding and modeling time series data

Requires familiarity with dynamical programming and reinforcement learning, which may necessitate prior coursework or experience in these areas

Presented by MIT, which is known for its rigorous approach to data science education and its contributions to statistical learning

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Learning Time Series with Interventions with these activities:

Review Linear Algebra Fundamentals

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Strengthen your understanding of linear algebra concepts, which are foundational for matrix completion methods used in time series prediction.

Browse courses on Linear Algebra

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Review matrix operations such as multiplication and inversion.
Study eigenvalues and eigenvectors and their applications.
Practice solving systems of linear equations.

Brush Up on Probability and Statistics

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Reinforce your knowledge of probability and statistics, essential for understanding the stochastic models and prediction methods used in time series analysis.

Browse courses on Probability

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Review probability distributions and their properties.
Study hypothesis testing and statistical inference.
Practice applying statistical methods to real-world datasets.

Read 'Time Series Analysis: With Applications in R'

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Gain a deeper understanding of time series analysis techniques and their applications using R.

View Time Series Analysis: With Applications in R... on Amazon

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Read the chapters on ARMA models and spectral analysis.
Work through the examples and exercises in the book.
Implement the techniques in R using real time series data.

Four other activities

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Implement ARMA Models

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Solidify your understanding of ARMA models by implementing them from scratch using Python or R.

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Write code to simulate ARMA processes with different parameters.
Implement algorithms to estimate ARMA model parameters from data.
Evaluate the performance of your ARMA models on real time series data.

Time Series Forecasting Competition

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Apply your knowledge of time series analysis to a real-world forecasting problem by participating in a competition on Kaggle or a similar platform.

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Choose a time series forecasting competition on Kaggle.
Download the data and explore the time series.
Build and train a time series forecasting model.
Submit your predictions and compare your performance to other participants.

Study 'Reinforcement Learning: An Introduction'

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Deepen your understanding of reinforcement learning algorithms for optimizing control and interventions in time series.

View Reinforcement Learning, second edition: An... on Amazon

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Read the chapters on dynamic programming and temporal-difference learning.
Work through the examples and exercises in the book.
Implement the reinforcement learning algorithms in Python or R.

Write a Blog Post on Time Series Interventions

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Consolidate your learning by writing a blog post explaining different methods for intervening in time series and their potential impact.

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Research different types of interventions in time series.
Choose a specific intervention method to focus on.
Write a clear and concise explanation of the method and its applications.
Include examples and visualizations to illustrate the concepts.

Career center

Learners who complete Learning Time Series with Interventions will develop knowledge and skills that may be useful to these careers:

Quantitative Analyst

A quantitative analyst, often called a quant, develops and implements mathematical and statistical models for financial markets, and time series analysis is an essential part of this work. This course's deep dive into learning structured models, prediction, and optimal intervention through reinforcement learning provides a strong base for quantitative roles. Much of the work in quantitative finance involves predicting fluctuations in stock prices, currency values, and other financial assets, all of which are time series. This course, which covers time-stamped observations and their dependence, is thus very useful. The course's coverage of practical applications through hands-on projects further prepares an individual for quantitative work.

See salaries and explore the career path for Quantitative Analyst

Data Scientist

A data scientist employs statistical methods to glean insights from data, and this course provides a solid foundation in time series analysis, a critical skill for many data science roles. Time series data, as covered in this course, is common in many fields, and the ability to model, predict, and intervene on such data is highly valuable. The course emphasis on learning structured models, prediction methods like matrix completion, and optimal intervention using reinforcement learning techniques directly contributes to a data scientist's analytical toolkit. The hands-on projects are especially useful for budding data scientists, who must be able to apply their knowledge.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A machine learning engineer builds and deploys machine learning models, and this course in time series will prove helpful for those in this role that work with temporal data. This course's treatment of time series methods, including learning structured models and prediction techniques, aligns with the responsibilities of a machine learning engineer. The study of optimal intervention strategies using reinforcement learning, as covered in this course, is also relevant. The practical, hands-on projects in this course are very useful for a machine learning engineer, who needs to be able to implement and test machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Statistician

A statistician develops and applies statistical theories and methods to collect, analyze, and interpret data, and this course offers applicable tools and methods for a statistician to analyze time-series data. The course's coverage of learning structured models, prediction, and optimal intervention is directly relevant to a statistician's analytical toolkit. This course, with its focus on time series analysis, would be particularly helpful for statisticians who work with dynamic data that evolves over time. The course's hands-on projects, and its use of real data, offer useful practical experience. This course will provide helpful knowledge.

See salaries and explore the career path for Statistician

Operations Research Analyst

An operations research analyst uses mathematical and analytical techniques to optimize processes and solve complex problems, and the content of this course will be useful to an operations research analyst who works with time-dependent data. The study of time series modeling and prediction, including the methods of matrix completion, offered in this course, can help an operations research analyst in their work. The techniques for intervention and control using dynamical programming and reinforcement learning are especially relevant. An operations research analyst would be wise to take a course that gives much attention to time-stamped data. The projects are very good experience.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

A financial analyst evaluates financial data, identifies trends, and makes recommendations to organizations, and the study of time series is relevant to these responsibilities. This course's focus on time series analysis will be valuable for financial analysts looking to understand market behavior and trends in financial data. The course's exploration of prediction and intervention methods, including matrix completion and reinforcement learning, are applicable to forecasting financial outcomes or evaluating investment strategies. A financial analyst will find the analysis of time-stamped observations, and the modeling of their dependence, to be highly relevant. The course provides much relevant material.

See salaries and explore the career path for Financial Analyst

Econometrician

An econometrician uses statistical methods to analyze economic data, and this course will help an econometrician in their analysis of time-dependent economic data. This course is relevant for those in econometrics because it focuses on modeling, predicting, and intervening upon time series data. The course covers many topics essential to econometric work, such as analyzing structural models, predicting future values using past data, and techniques for policy intervention. Econometricians depend on their knowledge of the dependence of time-stamped observations. This course provides material that directly meets this need. This course may prove helpful.

See salaries and explore the career path for Econometrician

Risk Analyst

A risk analyst assesses and manages potential risks for an organization, and this course may be helpful to those who work with time series data. The course's treatment of prediction methods and optimal intervention strategies using reinforcement learning can provide the tools a risk analyst needs to forecast potential risks and create mitigation strategies. A risk analyst uses data to assess and quantify risks and the study of time-stamped observations is one potential source of information. The course's emphasis on real-world projects enhances the learning experience. This course may be useful.

See salaries and explore the career path for Risk Analyst

Business Intelligence Analyst

A business intelligence analyst analyzes business data to identify trends and patterns, and this course may be useful for an analyst who uses time series data for this work. This course introduces time series analysis, which will be helpful for a business intelligence analyst looking to make sense of data that changes over time. This course’s discussion of prediction using past observations, and its exploration of intervention, is very applicable to this kind of work. This course, with its focus on time-stamped observations, particularly where there are dependencies between them, will help business intelligence analysts understand changes in their data over time. This course may be useful.

See salaries and explore the career path for Business Intelligence Analyst

Research Scientist

A research scientist conducts experiments and analyzes data to advance scientific knowledge, and this course may be useful for those who work with time-series data. The course's treatment of learning structured models, prediction methods, and optimal intervention strategies is relevant for a research scientist. The course's focus on modeling time-stamped observations and their dependencies is valuable for scientists working with complex, dynamic data. A research scientist who collects time-series data would find this course helpful. This course may be useful.

See salaries and explore the career path for Research Scientist

Bioinformatician

A bioinformatician uses computational methods to analyze biological data, and a course in time series may be helpful for those who manage biological data that varies over time. The course content, which covers time series analysis techniques, such as prediction and modeling, is applicable to certain types of bioinformatic research. The course might be useful to bioinformaticians who want to strengthen their knowledge of model-building. If a bioinformatician were working with time-series data, this course may be helpful. This course may be useful.

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Climate Scientist

A climate scientist studies long-term weather patterns, and a course in time series may be useful because much climate data exhibits behavior over time. This course's focus on time series analysis, including learning model structures and making predictions, is relevant. The course could strengthen a climate scientist's ability to model climate trends, and understand time-based fluctuations. A climate scientist might find the course helpful as they often deal with many types of time-stamped observational data. This course may be useful.

See salaries and explore the career path for Climate Scientist

Epidemiologist

An epidemiologist studies the patterns, causes, and effects of health conditions, and this course in time series may be applicable to tracking the spread of disease, for example. The course’s emphasis on time series analysis, and its discussion of prediction and optimal intervention, could help an epidemiologist model disease outbreaks and evaluate public health interventions. The course may be helpful to an epidemiologist whose work involves tracking disease trends over time. This course may be useful.

See salaries and explore the career path for Epidemiologist

Systems Analyst

A systems analyst assesses computer systems and processes, and this course may be useful for systems analysts who analyze time-based performance data. The course may help a systems analyst develop an understanding of time series data, as such an understanding is useful in many contexts. This course may apply to systems analysts who monitor metrics that exhibit variations over time. This course may be useful.

See salaries and explore the career path for Systems Analyst

Market Research Analyst

A market research analyst studies market conditions to determine the potential sales of a product or service, and this course may be useful to a market research analyst who deals with time series data. The course's focus on prediction and modeling may equip a market research analyst to better forecast market trends. Those in this role may use time-series data to understand changes in sales, and in consumer behavior. This course may be useful.

See salaries and explore the career path for Market Research Analyst

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Learning Time Series with Interventions.

Time Series Analysis

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Provides a comprehensive overview of time series analysis techniques with a focus on practical applications using the R programming language. It covers topics such as ARMA models, spectral analysis, and forecasting methods. This book valuable resource for understanding the theoretical foundations of time series analysis and implementing these techniques in real-world scenarios. It is commonly used as a textbook in academic institutions.

Time Series Analysis: With Applications in R...

Hardcover

$$$

Reinforcement Learning, second edition

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Comprehensive introduction to reinforcement learning, covering topics such as dynamic programming, Monte Carlo methods, and temporal-difference learning. It provides a solid foundation for understanding the algorithms used to optimize control and interventions for time series. This book is commonly used as a textbook in academic institutions and by industry professionals.

Reinforcement Learning, second edition: An...

Hardcover

$$$

Reinforcement Learning, second edition: An...

Kindle Edition

$$$

Learning Time Series with Interventions

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