Statistical and machine learning forecasting methods concerns and ways forward

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statistical and machine learning forecasting methods concerns and ways forward

Evangelos Spiliotis ( of Statistical and Machine Learning forecasting methods)

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Published 07.01.2019

Time Series Prediction

Machine Learning (ML) methods have been proposed in the academic literature as forecasting methods: Concerns and ways forward.
Evangelos Spiliotis

Comparing Classical and Machine Learning Algorithms for Time Series Forecasting

This question is obviously a very broad one, and is to some extent subjective. I do not pretend to do this broad question justice, or to provide a comprehensive answer to it. Instead, here I will present two very useful ways of looking at the question of Time Series analysis and ML methods. I consider these two ways of looking at the question useful, because keeping them in mind will allow you to better decide the question of what approach to use when trying to solve a time series analysis problem. Using this classification, the main difference between the two categories is that in the former case, the models are parametric, i. For this case we can consider them non parametric in the sense that can approximate any function to an arbitrary level of precision and complexity.

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 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.

Why Machine Learning is more Practical than Econometrics in the Real World

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 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.

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4 thoughts on “Evangelos Spiliotis ( of Statistical and Machine Learning forecasting methods)

  1. The field of statistical forecasting has progressed a great deal since the early dates when [70].

  2. Machine Learning ML methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting.

  3. In many business environments a data scientist is responsible for generating hundreds or thousands possibly more forecasts for an entire company, opposed to a single series forecast.

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