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This chapter will discuss real-time forecasting in a macroeconomic policy context. I will begin by talking about the Survey of Professional Forecasters (SPF), a survey of private-sector forecasters. Next, I will discuss research on real-time data analysis and its importance in forecasting. Finally, I will discuss real-time forecasting in the 1990s.

Papers presented at the Forecasting Financial Markets in India, held at Kharagpur during 29-31 December 2008

This open access book presents new developments in the field of demographic forecasting, covering both mortality, fertility and migration. For each component emerging methods to forecast them are presented. Moreover, instruments for forecasting evaluation are provided. Bayesian models, nonparametric models, cohort approaches, elicitation of expert opinion, evaluation of probabilistic forecasts are some of the topics covered in the book. In addition, the book is accompanied by complementary material on the web allowing readers to practice with some of the ideas exposed in the book. Readers are encouraged to use this material to apply the new methods to their own data. The book is an important read for demographers, applied statisticians, as well as other social scientists interested or active in the field of population forecasting. Professional population forecasters in statistical agencies will find useful new ideas in various chapters.

Intro -- CONTENTS -- PREFACE -- Fuzzy Time Series Forecasting Models Evaluation Based on A Novel Distance Measure -- Cagdas Hakan Aladag1,* and I. Burhan Turksen2 -- INTRODUCTION -- THE PROPOSED DISTANCE MEASURE AND THE SUGGESTED PERFORMANCE CRITERION -- THE APPLICATION -- CONCLUDING REMARKS -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A New Fuzzy Time Series Forecasting Model with Neural Network Structure -- Eren Bas* and Erol Egrioglu -- INTRODUCTION -- PROPOSED METHOD -- APPLICATION -- CONCLUSIONS AND DISCUSSIONS -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Two Factors High Order Non Singleton Type-1 and Interval Type-2 Fuzzy Systems for Forecasting Time Series with Genetic Algorithm -- M.H. Fazel Zarandi1, *, M. Yalinezhaad1 and I.B. Turksen2 -- INTRODUCTION -- Interval Type-2 Fuzzy Logic Sets and Systems -- Type-2 Fuzzy Logic Sets -- Non Singleton Interval Type-2 Fuzzy Logic Systems -- Determination of Footprints of Uncertainty (Umf and Lmf) in Interval Type-2 Fuzzy Logic Sets -- Fundamental Concepts of Fuzzy Time Series -- Proposed Two Factors High Order Non Singletontype-1 and Interval Type-2 Fuzzy Time Series Systems -- Tuning Method for Type-1 and Interval Type-2 FTSs with Genetic Algorithm -- Experimental Results by Temperature Prediction and TAIEX Forecasting -- Temperature Prediction with Proposed Method -- TAIEX Forecasting By Applying the Proposed Method with Genetic Algorithm -- GA Procedure -- Selection and Pairing -- Crossover -- Mutation and Reinsertion -- Termination Condition -- Type Reduction and Defuzzification -- CONCLUSION AND FUTURE WORKS -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A New Neural Network Model with Deterministic Trend and Seasonality Components for Time Series Forecasting -- Erol Egrioglu1,*, Cagdas Hakan Aladag2, Ufuk Yolcu3, Eren Bas1 and Ali Z. Dalar1.

Business forecasting is of extreme importance to managers at practically all levels.It is required for top managers to make long-term strategic decisions. Middlemanagement uses sales forecasts to develop their departmental budgets. Everyother plan such as a production plan, purchasing plan, manpower plan, and financialplan follows from sales forecasting. The book is designed for business professionalssuch as director of forecasting and planning, forecast manager, director of strategicplanning, director of marketing, sales manager, advertising manager, CFO, financialofficer, controller, treasur

Why do we need forecasts? --How do we make forecasts? --Where are we before we forecast? --How do we judge forecasts? --How uncertain are our forecasts? --Are some real world events unpredictable? --Why do systematic forecast failures occur? --Can we avoid systematic forecast failures? --How do we automatically detect breaks? --Can we forecast breaks before they hit? --Can we improve forecasts during breaks? --Would more information be useful? --Can econometrics improve forecasting? --Can you trust economic forecasts?