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.

Forecasting: theory and practice

Electricity market prices are highly volatile and often have high spikes. Both government authorities and market participants require sophisticated models and techniques for forecasting future prices and managing relevant financial risks in such a volatile market. This article extends the conditional autoregressive geometric process (CARGP) model (Chan et al., 2012) to the CARGP model with thresholds and jumps, which is abbreviated as CARGP-TJ model in this article. We will demonstrate that the proposed CARGP-TJ model not only captures the unique features of the electricity price but also performs better than other existing models. For robustness consideration, a heavy-tailed error distribution is adopted. Model implementation relies on the powerful Bayesian Markov chain Monte Carlo simulation techniques via WinBUGS software. The analysis of the daily maximum electricity prices of the New South Wales, Australia reveals that the proposed CARGP-TJ model captures the price spikes well for both in-sample esti...

Modeling Electricity Price Using A Threshold Conditional Autoregressive Geometric Process Jump Model

Quantile range-based volatility measure for modelling and forecasting volatility using high frequency data

We propose the Binary Geometric Process (BGP) model for longitudinal binary data with trends. The Geometric Process (GP) model contains two components to capture the dynamics on a trend: the mean of an underlying renewal process and the ratio which measures the direction and strength of the trend. The GP model is extended to binary data using a latent GP. The statistical inference for the BGP models is conducted using the least-square, maximum likelihood (ML) and Bayesian methods. The model is demonstrated through simulation studies and real data analyzes. Results reveal that all estimators perform satisfactorily and that the ML estimator performs the best. Moreover the BGP model is better than the ordinary logistic regression model.

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C. P. Y. Lam

Papers

A Bayesian conditional autoregressive geometric process model for range data