Results 1 to 10 of about 70 (69)
Forecast with forecasts: Diversity matters [PDF]
European Journal of Operational Research, 2022 Forecast combinations have been widely applied in the last few decades to improve forecasting. Estimating optimal weights that can outperform simple averages is not always an easy task. In recent years, the idea of using time series features for forecast combination has flourished.
Yanfei Kang +3 more
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Argumentative Forecasting [PDF]
International Joint Conference on Autonomous Agents and Multiagent Systems, 2022 We introduce the Forecasting Argumentation Framework (FAF), a novel argumentation framework for forecasting informed by recent judgmental forecasting research. FAFs comprise update frameworks which empower (human or artificial) agents to argue over time with and about probability of scenarios, whilst flagging perceived irrationality in their behaviour ...
Irwin, Benjamin +2 more
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Using Forecasts of Forecasters to Forecast [PDF]
SSRN Electronic Journal, 2006 Quantification techniques are popular methods in empirical research to aggregate the qualitative predictions at the micro-level into a single figure. In this paper, we analyze the forecasting performance of various methods that are based on the qualitative predictions of financial experts for major financial variables and macroeconomic aggregates ...
Nolte, Ingmar, Pohlmeier, Winfried
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Forecasting FOMC Forecasts [PDF]
SSRN Electronic Journal, 2018 (Hendry 1980, p. 403) The three golden rules of econometrics are “test, test, and test”. The current paper applies that approach to model the forecasts of the Federal Open Market Committee over 1992–2019 and to forecast those forecasts themselves.
S. Yanki Kalfa, Jaime Marquez
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Forecasting Professional Forecasters [PDF]
Journal of Business & Economic Statistics, 2006 Survey of forecasters, containing respondents' predictions of future values of growth, inflation and other key macroeconomic variables, receive a lot of attention in the financial press, from investors, and from policy makers. They are apparently widely perceived to provide useful information about agents' expectations.
Eric Ghysels, Jonathan H. Wright
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The Econometrics Journal, 2004 Summary: We consider forecasting using a combination, when no model coincides with a non-constant data generation process (DGP). Practical experience suggests that combining forecasts adds value, and can even dominate the best individual device. We show why this can occur when forecasting models are differentially misspecified, and is likely to occur ...
David Hendry, Michael Clements
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Forecasting Breaks and Forecasting During Breaks [PDF]
, 2012 AbstractThis article, which proposes a new approach to forecasting breaks by focusing on the role of information, begins by outlining the eight conditions that are necessary to successfully forecast a break. Section 2 then considers the concepts of unpredictability and information to address the first necessary condition.
Jennifer Castle +2 more
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For2For: Learning to forecast from forecasts
CoRR, 2020 This paper presents a time series forecasting framework which combines standard forecasting methods and a machine learning model. The inputs to the machine learning model are not lagged values or regular time series features, but instead forecasts produced by standard methods.
Shi Zhao, Ying Feng
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Ecology Letters, 2022 Abstract Encouraged by decision makers’ appetite for future information on topics ranging from elections to pandemics, and enabled by the explosion of data and computational methods, model‐based forecasts have garnered increasing influence on a breadth of decisions in modern society.
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The American Statistician, 2017 Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high quality forecasts — especially when there are a variety of time series and analysts with expertise in time series modeling are ...
Taylor, Sean J, Letham, Benjamin
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