Forecasting: theory and practice

Abstract

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 of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

References in corpus (17)

• Distributed Representations of Sentences and Documents
• The pseudo-marginal approach for efficient Monte Carlo computations
• Variable selection in nonparametric additive models
• Kaggle forecasting competitions: An overlooked learning opportunity
• Workload forecasting for a call center: Methodology and a case study
• GluonTS: Probabilistic Time Series Models in Python
• Deep Factors for Forecasting
• Forecasting time series of inhomogeneous Poisson processes with application to call center workforce management
• Gibbs posterior concentration rates under sub-exponential type losses
• Exit polling and racial bloc voting: Combining individual-level and R$\times$C ecological data
• Bayesian nonparametric forecasting of monotonic functional time series
• The Normal, the Natural, and the Harmonic
• Synergy in fertility forecasting: Improving forecast accuracy through model averaging
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• Textual Data for Time Series Forecasting
• Adaptive Dynamic Model Averaging with an Application to House Price Forecasting

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• CRPS Learning
• M5 Competition Uncertainty: Overdispersion, distributional forecasting, GAMLSS and beyond
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• A Study of Joint Graph Inference and Forecasting
• Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures
• Forecast with Forecasts: Diversity Matters
• Meta-Forecasting by combining Global Deep Representations with Local Adaptation
• TimeGym: Debugging for Time Series Modeling in Python
• Are renewable energies on a sustained path? Analysis of selected case-studies from the pre-pandemic-era