Dr Ivan Svetunkov
LecturerCareer Details
Ivan graduated from Saint-Petersburg State University of Economics and Finance (SPbSUEF) in 2006 and defended the candidate dissertation in Economics in 2008. The thesis topic was "The Complex Variables Production Functions", which proposed a new type of production functions using complex numbers theory.
After that, Ivan worked as a lecturer in Higher School of Economics, Saint-Petersburg and partially in SPbSUEF, teaching such courses as Microeconomics, Statistics, Econometrics, Financial Mathematics and Forecasting.
In 2013, Ivan joined Lancaster Centre for Forecasting (Lancaster University, UK) as a PhD student. He has developed a new type of exponential smoothing called "Complex Exponential Smoothing".
As a Research Associate, Ivan developed an intermittent state-space model (in collaboration with John Boylan) and worked on ARIMA implementation for Demand Works company.
As a lecturer in Management Science Department, Ivan started working in marketing analytics and marketing research areas.
He maintains three R packages: "smooth", implementing and developing state-space models for time series analysis and forecasting purpose, "greybox" for model building and forecasting using regression and "legion", implementing multivariate models for forecasting (all available in CRAN). He also works in C++ and Java.
Ivan has near 30 publications in the Russian language, including the textbook "Methods of Forecasting" written in co-authorship with Sergey Svetunkov. He is also the author of the online textbook "Forecasting and Analytics with ADAM".
His scientific interests include forecasting, statistical modelling, marketing analytics, time series analysis, and the application of complex-valued models in business and economics.
Research Overview
Ivan currently works in the area of forecasting, developing various models with application to the supply chain area. He focuses on state-space models and is also interested in time series analysis and econometrics. The areas of his interests also include marketing analytics and marketing research.
The following general areas are of the main interest to him at the moment:
- Intermittent demand,
- Multivariate models (vector models),
- Advanced estimators (multiple steps ahead and non-MSE based),
- Time-varying parameters models,
- Promotional modelling,
- Likelihood approach and information theory.
Additional Information
For some more information about me see my Linkedin page, my twitter or my forecasting related website.
PhD Supervision Interests
I am looking for PhD students eager to work in the areas of Statistical Methods of Forecasting, Marketing Analytics and Time Series Analysis. The perfect candidate should not be scared of mathematics (are you comfortable with linear algebra, probability theory, mathematical statistics and information theory?) and should know or be ready to learn how to program (preferably in R or Python). NOTE: If you want to apply for PhD under my supervision, you need to answer the following question in the first email you send me: When, by whom and in what paper was the Single Source of Error state space model first proposed?
Understanding and Modelling Electric Vehicle (EV) Charging Behaviour Using Choice Modelling
25/08/2020 → 31/03/2021
Research
Newsvendor Problems
01/01/2020 → 31/12/2022
Research
Using ARIMA models in supply chain forecasting
13/07/2016 → 01/09/2020
Research
Intermittent state-space model
01/04/2016 → …
Research
Trace Forecast Likelihood
13/12/2014 → 07/07/2023
Research
Complex Exponential Smoothing
01/10/2013 → …
Research
Incorporating Parameters Uncertainty in ETS
Oral presentation
43rd International Symposium on Forecasting
Participation in workshop, seminar, course
Demand Forecasting with SAP IBP
Business Course/Training
Benchmarking intermittent demand forecasting approaches for The Math Company
Consultancy
iETS: State Space Model for Intermittent Demand Forecasting
Oral presentation
Naval Research Logistics (Journal)
Publication peer-review
Connecting the dots: how to make ETS work with ARIMA
Oral presentation
42nd International Symposium on Forecasting
Symposium
2022 STOR-i Workshop on Prediction and Optimisation
Other
How to Make Multiplicative ETS Work for You
Oral presentation
Analysis of several machine learning algorithms for Demand Works company
Consultancy
CMAF Friday Forecasting Talks
Participation in workshop, seminar, course
useR! 2019
Participation in conference -Mixed Audience
What about those sweet melons? Using mixture models for demand forecasting in retail
Oral presentation
ISF 2018 International Symposium on Forecasting
Participation in conference -Mixed Audience
40th ISMS Marketing Science Conference
Participation in conference -Mixed Audience
ISF 2017 International Symposium on Forecasting
Participation in conference -Mixed Audience
IIF Workshop on Supply Chain Forecasting for Operations
Participation in workshop, seminar, course
ISF 2016 International Symposium on Forecasting
Participation in conference -Mixed Audience
27th European Conference on Operational Research
Participation in conference -Mixed Audience
ISF 2015 - 35th International Symposium on Forecasting
Participation in conference -Mixed Audience
ISF 2014 - 34th International Symposium on Forecasting
Participation in conference -Mixed Audience
Centre for Marketing Analytics & Forecasting
Centre for Marketing Analytics & Forecasting
- Centre for Health Futures
- Centre for Marketing Analytics & Forecasting
- DSI - Foundations
- Lancaster Intelligent, Robotic and Autonomous Systems Centre
- LIRA - Fundamentals
- LIRA - Society and Human Behaviour
- STOR-i Centre for Doctoral Training