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 (53)

• Deep Learning with Differential Privacy
• Distributed Representations of Sentences and Documents
• Variational Inference: A Review for Statisticians
• A survey of cross-validation procedures for model selection
• Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions
• Deep learning to represent sub-grid processes in climate models
• Deep Learning for Short-Term Traffic Flow Prediction
• The pseudo-marginal approach for efficient Monte Carlo computations
• Variable selection in nonparametric additive models
• Highly comparative time-series analysis: The empirical structure of time series and their methods
• Highly comparative feature-based time-series classification
• Critical Market Crashes
• Comparison of Bayesian predictive methods for model selection
• Kaggle forecasting competitions: An overlooked learning opportunity
• Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling
• Tuning parameter selection in high dimensional penalized likelihood
• Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks
• LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns
• Probabilistic Mid- and Long-Term Electricity Price Forecasting
• Multivariate probabilistic forecasting using Bayesian model averaging and copulas
• Electricity Price Forecasting using Sale and Purchase Curves: The X-Model
• Dynamic Bayesian Predictive Synthesis in Time Series Forecasting
• Econometric modelling and forecasting of intraday electricity prices
• On the statistical properties and tail risk of violent conflicts
• GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
• Prediction of functional ARMA processes with an application to traffic data
• Reconciliation of probabilistic forecasts with an application to wind power
• Forecasting with time series imaging
• Workload forecasting for a call center: Methodology and a case study
• GluonTS: Probabilistic Time Series Models in Python
• Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories
• Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting
• Bayesian forecasting of many count-valued time series
• Deep Factors for Forecasting
• An Innovations Algorithm for the prediction of functional linear processes
• Forecasting time series of inhomogeneous Poisson processes with application to call center workforce management
• Forecasting Intermittent Demand by Hyperbolic-Exponential Smoothing
• Statistics of extinction and survival in Lotka-Volterra systems
• Polyhedral Predictive Regions For Power System Applications
• Gibbs posterior concentration rates under sub-exponential type losses
• Exit polling and racial bloc voting: Combining individual-level and R$\times$C ecological data
• Forecasting age distribution of death counts: An application to annuity pricing
• Modeling competition between two pharmaceutical drugs using innovation diffusion models
• The Impact of Renewable Energy Forecasts on Intraday Electricity Prices
• Bayesian nonparametric forecasting of monotonic functional time series
• Improving forecasting performance using covariate-dependent copula models
• The Normal, the Natural, and the Harmonic
• Forecasting functional time series using weighted likelihood methodology
• Synergy in fertility forecasting: Improving forecast accuracy through model averaging
• Retiree mortality forecasting: A partial age-range or a full age-range model?
• Probabilistic Forecasting of Patient Waiting Times in an Emergency Department
• Textual Data for Time Series Forecasting
• Adaptive Dynamic Model Averaging with an Application to House Price Forecasting

Cited by in corpus (11)

• Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
• Distributed ARIMA Models for Ultra-long Time Series
• CRPS Learning
• M5 Competition Uncertainty: Overdispersion, distributional forecasting, GAMLSS and beyond
• Deep Explicit Duration Switching Models for Time Series
• 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