Spyros Makridakis

Bio: Spyros Makridakis is an academic researcher from University of Nicosia. The author has contributed to research in topics: Probabilistic forecasting & Competition (economics). The author has an hindex of 50, co-authored 146 publications receiving 15460 citations. Previous affiliations of Spyros Makridakis include Georgetown University & European Institute.

Forecasting: Methods and Applications

Abstract: Presents a wide range of forecasting methods useful for undergraduate or graduate students majoring in business management, economics, or engineering. Develops skills for selecting the proper methodology. Integrates forecasting with the planning and decision-making activities within an organization. Methods of forecasting include: decomposition, regression analysis, and econometrics. Stresses the strengths and weaknesses of the individual methods in various types of organizational areas. Numerous examples are included.

The accuracy of extrapolation (time series) methods: Results of a forecasting competition

Abstract: ln the last few decades matiy methods have become available for forecasting. As always, when alternatives exist, choices need to be made so that an appropriate forecasting method can be selected and used for the specific situation being considered. This paper reports the results of a forecasting competition that provides information to facilitate such choice. Seven experts in each of the 24 methods forecasted up to 1001 series for six up to eighteen time horizons. The results of the competition are presented in this paper whose purpose is to provide empirical evidence about differences found to exist among the various extrapolative (time series) methods used in the competition.

Statistical and Machine Learning forecasting methods: Concerns and ways forward.

Abstract: Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Modern Applied Statistics With S

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Causation and Effectuation: Toward a Theoretical Shift from Economic Inevitability to Entrepreneurial Contingency

Abstract: In economics and management theories, scholars have traditionally assumed the existence of artifacts such as firms/organizations and markets. I argue that an explanation for the creation of such artifacts requires the notion of effectuation. Causation rests on a logic of prediction, effectuation on the logic of control. I illustrate effectuation through business examples and realistic thought experiments, examine its connections with existing theories and empirical evidence, and offer a list of testable propositions for future empirical work.

Simple Heuristics That Make Us Smart

Abstract: Fast and frugal heuristics - simple rules for making decisions with realistic mental resources - are presented here. These heuristics can enable both living organisms and artificial systems to make smart choices, classifications, and predictions by employing bounded rationality. But when and how can such fast and frugal heuristics work? What heuristics are in the mind's adaptive toolbox, and what building blocks compose them? Can judgments based simply on a single reason be as accurate as those based on many reasons? Could less knowledge even lead to systematically better predictions than more knowledge? This book explores these questions by developing computational models of heuristics and testing them through experiments and analysis. It shows how fast and frugal heuristics can yield adaptive decisions in situations as varied as choosing a mate, dividing resources among offspring, predicting high school drop-out rates, and playing the stock market.