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.

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Real estate appraisal: a review of valuation methods. Elli Pagourtzi and Vassilis Assimakopoulos.Real estate appraisal, property valuation or land valuation is the process of. Have provided a review on the methods used in the industry by. Of the Treasury Blueprint for a Modernized Financial Regulatory Structure PDF.Real Estate Appraisals: Common Issues and Best Practices. In our review of appraisal reports, we are finding that although many appraisals contain a. of this valuation method from the appraisal requires that the appraiser discuss the.

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