Statistical forecasting

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“Calibeating”: Beating forecasters at their own game

Dean Foster, Sergiu Hart

Theoretical Economics

2023

In order to identify expertise, forecasters should not be tested by their calibration score, which can always be made arbitrarily small, but rather by their Brier score. The Brier score is the sum of the calibration score and the refinement score; the latter measures how good the sorting into bins with the same forecast is, and thus attests to “expertise.” This raises the question of whether one can gain

Economics
ARIMAX model for forecasting maintenance work (AMFM): A multi-stage seasonal ARIMAX model for work order time series forecasting

Anupam Purwar, Matthew Reimherr

14th Edition of the Conference on the Digital Economy (CODE)

2023

E-commerce business depends on smooth day to day functioning of its warehouses/facilities. The functioning of these facilities depend on the health of material handling equipment. To keep these equipment healthy, these facilities employ the help of maintenance engineers who perform predictive/breakdown maintenance work. To ensure an effective maintenance operation necessitates efficient planning of maintenance

Machine learning
Causal forecasting: Generalization bounds for autoregressive models

Leena Chennuru Vankadara, Philipp Faller, Michaela Hardt, Lenon Vogel, Debarghya Ghoshdastidar, Dominik Janzing

UAI 2022

2022

Despite the increasing relevance of forecasting methods, causal implications of these algorithms remain largely unexplored. This is concerning considering that, even under simplifying assumptions such as causal sufficiency, the statistical risk of a model can differ significantly from its causal risk. Here, we study the problem of causal generalization—generalizing from the observational to interventional

Machine learning
Domain adaptation for time series forecasting via attention sharing

Xiaoyong Jin, Youngsuk Park, Danielle Maddix Robinson, Hao Wang, Yuyang (Bernie) Wang

ICML 2022

2022

Recently, deep neural networks have gained increasing popularity in the field of time series forecasting. A primary reason for their success is their ability to effectively capture complex temporal dynamics across multiple related time series. The advantages of these deep forecasters only start to emerge in the presence of a sufficient amount of data. This poses a challenge for typical forecasting problems

Machine learning
Forecast hedging and calibration

Dean Foster, Sergiu Hart

Journal of Political Economy

2021

Calibration means that forecasts and average realized frequencies are close. We develop the concept of forecast hedging, which consists of choosing the forecasts so as to guarantee that the expected track record can only improve. This yields all the calibration results by the same simple basic argument while differentiating between them by the forecast-hedging tools used: deterministic and fixed point based

Economics
Forecasting: theory and practice

Tim Januschowski, 80 co-authors

International Journal of Forecasting

2021

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review

Machine learning
On the classification of ML and statistical forecasting methods: comments and alternatives

Tim Januschowski, Laurent Callot, Jan Gasthaus, Valentin Flunkert, David Salinas, Yuyang (Bernie) Wang, Michael Bohlke-Schneider

International Journal of Forecasting

2019

Classifying forecasting methods as being either of a “machine learning” or “statistical” nature has become commonplace in parts of the forecasting literature and community, as exemplified by the M4 competition and the conclusion drawn by the organizers. We argue that this distinction does not stem from fundamental differences in the methods assigned to either class. Instead, this distinction is probably

Machine learning

Statistical forecasting

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