Forecasts guide decisions in all areas of economics and finance. Economic policy makers base their decisions on business cycle forecasts, investment decisions of firms are based on demand forecasts, and portfolio managers try to outperform the market based on financial market forecasts. Forecasts extract relevant information from the past and help to reduce the inherent uncertainty of the future. The topic of this special issue of the Journal of Economics and Statistics is the theory and practise of forecasting and forecast evaluation and an overview of the state of the art of forecasting.

Bring the latest statistical tools to bear on predicting future variables and outcomes A huge range of fields rely on forecasts of how certain variables and causal factors will affect future outcomes, from product sales to inflation rates to demographic changes. Time series analysis is the branch of applied statistics which generates forecasts, and its sophisticated use of time oriented data can vastly impact the quality of crucial predictions. The latest computing and statistical methodologies are constantly being sought to refine these predictions and increase the confidence with which important actors can rely on future outcomes. Time Series Analysis and Forecasting presents a comprehensive overview of the methodologies required to produce these forecasts with the aid of time-oriented data sets. The potential applications for these techniques are nearly limitless, and this foundational volume has now been updated to reflect the most advanced tools. The result, more than ever, is an essential introduction to a core area of statistical analysis. Readers of the third edition of Time Series Analysis and Forecasting will also find: Updates incorporating JMP, SAS, and R software, with new examples throughout Over 300 exercises and 50 programming algorithms that balance theory and practice Supplementary materials in the e-book including solutions to many problems, data sets, and brand-new explanatory videos covering the key concepts and examples from each chapter. Time Series Analysis and Forecasting is ideal for graduate and advanced undergraduate courses in the areas of data science and analytics and forecasting and time series analysis. It is also an outstanding reference for practicing data scientists.

This report describes the development of an early prototype expert system for the classical single-station weather forecasting problem. The skills required to maximize forecast quality based on such limited data are being lost though they continue to have application to certain military scenarios which include communications interruptions. Knowledge for the expert system was obtained from a meteorologist experienced in single-station weather forecasting by working through case study data. The prototype was developed on a microcomputer and uses forward and backward chaining rules, frames, and other functions and procedures written in LISP. The expert, system is able to emulate the expert for a several day case study used in the development, although much more work must be done to make the system useful for the general meteorological situation.

This book describes the stochastic and predictive control modelling of electrical systems that can meet the challenge of forecasting energy requirements under volatile conditions. The global electrical grid is expected to face significant energy and environmental challenges such as greenhouse emissions and rising energy consumption due to the electrification of heating and transport. Today, the distribution network includes energy sources with volatile demand behaviour, and intermittent renewable generation. This has made it increasingly important to understand low voltage demand behaviour and requirements for optimal energy management systems to increase energy savings, reduce peak loads, and reduce gas emissions. Electrical load forecasting is a key tool for understanding and anticipating the highly stochastic behaviour of electricity demand, and for developing optimal energy management systems. Load forecasts, especially of the probabilistic variety, can support more informed planning and management decisions, which will be essential for future low carbon distribution networks. For storage devices, forecasts can optimise the appropriate state of control for the battery. There are limited books on load forecasts for low voltage distribution networks and even fewer demonstrations of how such forecasts can be integrated into the control of storage. This book presents material in load forecasting, control algorithms, and energy saving and provides practical guidance for practitioners using two real life examples: residential networks and cranes at a port terminal.

The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.

In virtually every decision, a pharmaceutical executive considers some type of forecast. This process of predicting the future is crucial to many aspects of the company - from next month's production schedule, to market estimates for drugs in the next decade. The pharmaceutical forecaster needs to strike a delicate balance between over-engineering the forecast - including rafts of data and complex 'black box' equations that few stakeholders understand and even fewer buy into - and an overly simplistic approach that relies too heavily on anecdotal information and opinion. Art Cook's highly pragmatic guide explains the basis of a successful balanced forecast for products in development as well as currently marketed products. The author explores the pharmaceutical forecasting process; the varied tools and methods for new product and in-market forecasting; how they can be used to communicate market dynamics to the various stakeholders; and the strengths and weaknesses of different forecast approaches. The text is liberally illustrated with tables, diagrams and examples. The final extended case study provides the reader with an opportunity to test out their knowledge. Forecasting for the Pharmaceutical Industry is a definitive guide for forecasters as well as the multitude of decision makers and executives who rely on forecasts in their decision making.