Recent Posts

The 4th demcoratising forecasting workshop will take place in Ankara University in January 2019. Democratising forecasting is an intiaitive lead by Bahman Rostami-Tabar. The aim is to share knownoledge on forecasting and influence relevant practices prioritising the need of population and the society. Register here Registeration is closed on Monday 7th January 2019 at 02:30 (UK time). Registeration is confirmed once you attend the webinar. If you can not attend the webinar, please email me: rostami-tabarb@cardiff.


The 3rd demcoratising forecasting workshop will take place in Charmo University in October 2018. Democratising forecasting is a part of Forecasting4Change intiaitive lead by Bahman Rostami-Tabar. The aim is to share knownoledge on forecasting and influence relevant practices prioritising the need of population and the society. Register in Eventbrite Registeration is closed on Thursday 20 September 2018 at 02:30 (UK time). Prerequisites Basic knowledge in statistics; No knowledge of forecasting is assumed.


The first international workshop dedicated to “Forecasting for Social Good” took place at Cardiff Business School , 12-13 July 2018. We had 38 participants from 8 countries( UK, USA, Australia, India, Norway, Switzerland, India, Germany) over two days. In particular, we had practitionaires from organisations such as NHS, International Committee of the Red Cross, United Nation High Commission for Refugees, United Nations Office for Disaster Risk Reduction, Welsh Government, Australian Government, Future Generation Commissioner for Wales.


Forecasting for Social Good

One of the key objectives of my research is to use Operational Research techniques to improve decision making in organizations with social missions, thereby positively contributing to advancing knowledge in the field of forecasting practice for social good. My goal is to influence relevant practices in academia and organisations.

To that end, I have organised the first international workshop dedicated to the use of forecasting for social good in Cardiff University on Thursday and Friday 12-13 July 2018. Please see 24th IIF workshop on Forecasting for Social Good for more information.

This workshop was an important step to bring more attention to this extremely important topic. It brings together researchers and practitioners across 10 countries to discuss research agenda in this area, whilst helping me broaden my network and introduce important research collaborations with academics as well as build solid partnerships with organizations.

A new website dedicated to “forecasting for Social Good” is under constraction:

Democratising forecasting

This is an ongoing initiative, sponsored by the International Institute of Forecasters will provide cutting-edge training in the use of forecasting with R in some of the world’s least developed countries to transfer knowledge in forecasting and help decision makers use it as an effective tool to support decision-making process. R is an open source programming language and software widely used for data analysis, manipulation, visualisation and modelling.

“These workshops aim to provide up-to-date training on the principles of forecasting and create an international network to conduct research on forecasting with social impact for less developed countries.”

The workshops will be provided by Dr Bahman Rostami-Tabar whose research focuses on forecasting for social good, supply chain forecasting and the interface between forecasting and decision-making. For more information please see IIF Website

To organise a workshop in your country, contact


Selected Publications

Recent advances have demonstrated the benefits of temporal aggregation for demand forecasting, including increased accuracy, improved stock control and reduced modelling uncertainty. With temporal aggregation a series is transformed, strengthening or attenuating different elements and thereby enabling better identification of the time series structure. Two different schools of thought have emerged. The first focuses on identifying a single optimal temporal aggregation level at which a forecasting model maximises its accuracy. In contrast, the second approach fits multiple models at multiple levels, each capable of capturing different features of the data. Both approaches have their merits, but so far they have been investigated in isolation. We compare and contrast them from a theoretical and an empirical perspective, discussing the merits of each, comparing the realised accuracy gains under different experimental setups, as well as the implications for business practice. We provide suggestions when to use each for maximising demand forecasting gains.
In JBS, 2017

Demand forecasting performance is subject to the uncertainty underlying the time series an organization is dealing with. There are many approaches that may be used to reduce uncertainty and thus to improve forecasting performance. One intuitively appealing such approach is to aggregate demand in lower‐frequency “time buckets.” The approach under concern is termed to as temporal aggregation, and in this article, we investigate its impact on forecasting performance. We assume that the nonaggregated demand follows either a moving average process of order one or a first‐order autoregressive process and a single exponential smoothing (SES) procedure is used to forecast demand. These demand processes are often encountered in practice and SES is one of the standard estimators used in industry. Theoretical mean‐squared error expressions are derived for the aggregated and nonaggregated demand to contrast the relevant forecasting performances. The theoretical analysis is supported by an extensive numerical investigation and experimentation with an empirical dataset. The results indicate that performance improvements achieved through the aggregation approach are a function of the aggregation level, the smoothing constant, and the process parameters. Valuable insights are offered to practitioners and the article closes with an agenda for further research in this area.
In NRL, 2013

Recent Publications

. Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels?. In JBS, 2017.


. A novel ranking procedure for forecasting approaches using Data Envelopment Analysis. In TFSC, 2016.


. Non-stationary demand forecasting by cross-sectional aggregation. In IJPE, 2015.


. A note on the forecast performance of temporal aggregation. In NRL, 2014.


. Demand forecasting by temporal aggregation. In NRL, 2013.


Recent & Upcoming Talks

Forecasting using R in Senegal
Jun 23, 2018 9:00 AM


Subject I am teaching in 2018-2019 at Cardiff University :

BST832: Forecasting

The Forecasting module aims to provide students with an in-depth understanding of demand forecasting, and essential planning activity within the field of operations and supply chain management. It is taught by combining theoretical and applied approaches appropriate to an MSc audience and draws upon the latest forecasting techniques. It develops, applies and consolidates learning through solving problems related to real demand patterns. Specifically, it aims to:

  • Provide a systematic and comprehensive understanding of the use of forecasting methodologies in Operations Management and Logistics settings
  • Provide a comprehensive understanding of real industrial case studies using analytical modelling and simulation.
  • Provide a systematic capability to make decisions on the suitability of forecasting methods for the Operations Management and Forecasting problem in hand.
  • Develop the ability to originally apply innovative forecasting methodologies to operational problem solving.

BST835: Risk Management in supply Chains

This module will equip students with:

  • A comprehensive understanding of the major theoretical underpinnings of risk management strategies in a business and supply chain context.
  • An ability to critically evaluate the major debates within the topics.
  • Critical awareness of strategies, methods, tools and techniques that can be used to identify, analyse and respond to various risks.
  • Analytical skills in risk management by evaluating real manufacturing and service, public and private sector case studies.