Forecasting: theory and practice (2012.03854v4)

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.

• The paper reviews forecasting methodologies, contrasting classical models like ARIMA with innovative machine learning techniques.
• It details the challenges of intermittent and judgmental forecasting while integrating quantitative data with expert insights.
• The paper advocates for hybrid and real-time forecasting models to enhance demand prediction and supply chain management.

Overview of "Forecasting: Theory and Practice"

The paper "Forecasting: Theory and Practice," published in the International Journal of Forecasting, provides a comprehensive review of forecasting methodologies, both from theoretical and practical perspectives. This article is co-authored by an extensive group of researchers, each contributing to various aspects of forecasting, ranging from historical methodologies to modern advanced techniques.

Summary of Content

This extensive review is structured into multiple sections, each exploring key areas of forecasting. The paper begins with an exploration of the foundational theories behind forecasting, examining traditional statistical and econometric models such as ARIMA, state-space models, and exponential smoothing. These classical methods are celebrated for their interpretability and have been proven reliable across various forecasting tasks.

The discussion then transitions to more contemporary approaches, including machine learning and AI-driven models. These methods, while complex, offer significant potential given their ability to model intricate patterns and handle vast datasets. The paper acknowledges the challenges associated with these approaches, particularly around issues of overfitting and the need for substantial data to train such models effectively.

Intermittent demand forecasting is highlighted as a specific challenge, given its prevalence in industries such as manufacturing and retail. The paper reviews both parametric and non-parametric approaches, noting the importance of choosing the right model to capture the unique patterns of intermittent data.

Judgmental forecasting is covered extensively, emphasizing the role of expert intuition and the integration of qualitative insights with quantitative data. The authors discuss techniques like the Delphi method and structured analogies, noting their utility in contexts where historical data may be sparse or non-existent.

The paper also examines the role of forecasting in operational and supply chain management, stressing the impact of accurate demand forecasting on inventory management, production planning, and logistics. The authors underscore the importance of integrating hierarchical forecasting models to manage inventory across different levels of aggregation coherently.

Implications and Future Directions

The paper identifies several critical implications for both theoretical advancement and practical application in forecasting. One of the standout discussions revolves around the future trajectory of forecasting methods. The integration of big data analytics, combined with machine learning, is highlighted as a promising area, albeit one that requires careful consideration of data quality and the risk of overfitting.

The authors also speculate on the potential for hybrid models that combine the strengths of traditional statistical methods with machine learning algorithms to improve prediction accuracy while maintaining interpretability. The challenge lies in developing models that are not only accurate but also computationally efficient and interpretable.

In terms of practical application, the paper highlights the importance of real-time forecasting and dynamic models that can adjust to new data as it becomes available. This is crucial in industries where conditions change rapidly, and static models quickly become outdated.

Conclusion

"Forecasting: Theory and Practice" serves as an encyclopedic reference for both novice and seasoned researchers in the field. The paper underscores the complexity and multidisciplinary nature of forecasting, bridging traditional statistical methods with cutting-edge AI techniques. By critically evaluating existing methodologies and proposing future research directions, the authors provide a roadmap for developing robust, adaptable, and actionable forecasting models. This work emphasizes the ongoing need for innovations that address the evolving challenges of forecasting in a rapidly changing data landscape.