Principles of forecasting: A handbook for researchers and practitioners

By P. Goodwin, J. K. Ord, Lars-Erik Öller, J. Sniezek, M. Leonard. 2002

732 citations recorded by the source corpus.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/50f54898d8475e134c32496a92e93da67a1856a8

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In Management Science and Operations Research

Citations in the corpus

Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (2005, background): “…can be traced back to the extensive work in cognitive science [87], approximation theory [81], information retrieval [89], forecasting theories [6], and also have links to management science [71] and to consumer choice modeling in marketing [60], recommender systems emerged as an independent…”
Combining multiple probability predictions using a simple logit model (2014, background): “Many authors, including Armstrong (2001), Clemen (1989), and Graefe, Armstrong, Jones, and Cuzán (2014), have encouraged the use of equal weights unless there is strong evidence supporting unequal weightings of the experts.”
Exploiting big data in time series forecasting: A cross-sectional approach (2015, unknown): “Traditional techniques, such as Exponential Smoothing or ARIMA models [4], capture the historical behavior of a time series and model the dependency of future on past time series values.”
Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach (2006, methodology): “These models result from the time series analysis and forecasting methods which can be found in statistics and econometrics [2].”
Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference (2018, background): “The forecasting (i.e., time predictions) literature (reviewed in Armstrong 2001, Stock and Watson 2001, Timmermann 2006) offers two further approaches.”
The social psychology of the wisdom of crowds. (2012, background): “Subsequent research in the forecasting literature demonstrated that simple combination methods that weight people equally, such as averaging judgments, often perform as well as more sophisticated statistical methods of combination (Armstrong, 2001; Clemen, 1989; Makridakis & Winkler, 1983).”
Variance Stabilizing Transformations for Electricity Spot Price Forecasting (2018, background): “, when the input data has a finite variance [26].”
Intuitions About Combining Opinions: Misappreciation of the Averaging Principle (2006, background): “Extensive research in the forecasting (Armstrong, 2001; Clemen, 1989), decision making (Wallsten, Budescu, Erev, & Diederich, 1997), and groups (Einhorn, Hogarth, & Klempner, 1977; Gigone & Hastie, 1997) literatures has confirmed that averaging is a powerful and robust way of reducing error in…”
The functions and dysfunctions of hierarchy (2010, background): “, & Anderson, C. (2010). The downside of looking like a leader: Leaders’ powerful demeanor stifles follower voice in participative decision making.”
Characteristic-Based Clustering for Time Series Data (2006, methodology): “The features of trend, seasonality, periodic, serial correlation, skewness, and kurtosis have been widely used as exemplary measures in many time series feature-based research (Armstrong, 2001).”
Crowd-sourced Text Analysis: Reproducible and Agile Production of Political Data (2016, background): “Simple averaging of individual judgments is the benchmark when there is no additional information on the quality of individual coders (Armstrong 2001; Lyon and Pacuit 2013; Turner et al. 2014).”
Forecasting: theory and practice (2020, background): “Rowe and Wright (2001) mentioned that, by adopting the Delphi method, groups of individuals can produce more accurate forecasts than simply using unstructured methods.”
A Genetic Algorithm to Optimize Lazy Learning Parameters for the Prediction of Customer Demands (2013, methodology): “Hence, applied prediction methods have to consider the influences sufficiently, in order to achieve accurate forecasts [6].”