Forecasting foreign exchange rates using kernel methods

Sewell, Martin; Shawe‐Taylor, John

doi:10.1016/j.eswa.2012.01.026

Cited by 19 publications

(11 citation statements)

References 38 publications

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“…The answer in this case is a resounding no. This would be a contradiction to the No Free Lunch Theorem [31]. Although for many datasets this separation line does not exist, in these cases it is no longer The reader can find in [19] an example developed from beginning to end.…”

Section: Basic Elements Of the Minimalist Machine Learning Paradigmmentioning

confidence: 92%

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A Novel Data Analytics Method for Predicting the Delivery Speed of Software Enhancement Projects

et al. 2020

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A fundamental issue of the software engineering economics is productivity. In this regard, one measure of software productivity is delivery speed. Software productivity prediction is useful to determine corrective activities, as well as to identify improvement alternatives. A type of software maintenance is enhancement. In this paper, we propose a data analytics-based software engineering algorithm called search method based on feature construction (SMFC) for predicting the delivery speed of software enhancement projects. The SMFC belongs to the minimalist machine learning paradigm, and as such it always generates a two-dimensional model. Unlike the usual data analytics methods, SMFC includes an original algorithmic training procedure, in which both the independent and dependent variables are considered for transformation. SMFC prediction performance is compared to those of statistical regression, neural networks, support vector regression, and fuzzy regression. To do this, seven datasets of software enhancement projects obtained from the International Software Benchmarking Standards Group (ISBSG) Release 2017 were used. The validation method is leave-one-out cross validation, whereas absolute residuals have been chosen as the performance measure. The results indicate that the SMFC is statistically better than statistical regression. This fact represents an obvious advantage in favor of SMFC, because the other two methods are not statistically better than SMFC.

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Section: Basic Elements Of the Minimalist Machine Learning Paradigmmentioning

confidence: 92%

“…The result is shown below, with a w vector corresponding to a linear combination of the support vectors x i [31].…”

Section: Svrmentioning

confidence: 99%

A Novel Data Analytics Method for Predicting the Delivery Speed of Software Enhancement Projects

et al. 2020

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“…Woodward and Neil [34] and Sewell and Shawe-Taylor [39] both state that NFL is often misunderstood; Wolpert [16] writes that much research has arguably "missed the most important implications of the theorems". In this section, several actual and potential misinterpretations will be presented.…”

Section: Misunderstanding Nflmentioning

confidence: 99%

“…An algorithm must be specialised to a subset of problems (not a "problem domain", as discussed in The undefined terms "problem domain" and "problem-specific"), taking advantage of properties of that subset, to outperform random search on it. Contrary to Wolpert and Macready [2] and Sewell and Shawe-Taylor [39] it is not necessary to know or state the properties in question: an algorithm will perform as well as it performs, no matter what the user knows or states. Many users of generic algorithms which are specialised to problem subsets achieve good results without being capable of stating the structural properties of their objective functions.…”

Section: Avoiding Nfl: Assumptionsmentioning

confidence: 99%

“…Schaffer also pointed out that NFLML is a formalisation of the basic problem of induction-drawing any generalisation from observed data is impossible without an additional assumption or bias-and dates this to Hume [68]. In fact, according to Sewell and Shawe-Taylor [39], NFLML "formalizes Hume, extends him and calls all of science into question". We cannot learn (in the machine learning sense, or in the scientific sense of proceeding from observations to principles and predictions, or even in the everyday sense) without a suitable bias, and it is claimed that there is no a priori justification for choosing any bias.…”

Section: The Anthropic Principle and Nflmentioning

confidence: 99%

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When and Why Metaheuristics Researchers can Ignore “No Free Lunch” Theorems

McDermott

2020

SN COMPUT. SCI.

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The No Free Lunch (NFL) theorem for search and optimisation states that averaged across all possible objective functions on a fixed search space, all search algorithms perform equally well. Several refined versions of the theorem find a similar outcome when averaging across smaller sets of functions. This paper argues that NFL results continue to be misunderstood by many researchers, and addresses this issue in several ways. Existing arguments against real-world implications of NFL results are collected and restated for accessibility and new ones are added. Specific misunderstandings extant in the literature are identified, with speculation as to how they may have arisen. This paper presents an argument against a common paraphrase of NFL findings-that algorithms must be specialised to problem domains to do well-after problematising the usually undefined term "domain". It provides novel concrete counterexamples illustrating cases where NFL theorems do not apply. In conclusion, it offers a novel view of the real meaning of NFL, incorporating the anthropic principle and justifying the position that in many common situations researchers can ignore NFL.

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Analysis and Prediction About the Relationship of Foreign Exchange Market Sentiment and Exchange Rate Trend

Zhang

2018

Advances in Intelligent Systems and Computing

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Forecasting foreign exchange rates using kernel methods

Cited by 19 publications

References 38 publications

A Novel Data Analytics Method for Predicting the Delivery Speed of Software Enhancement Projects

A Novel Data Analytics Method for Predicting the Delivery Speed of Software Enhancement Projects

When and Why Metaheuristics Researchers can Ignore “No Free Lunch” Theorems

Analysis and Prediction About the Relationship of Foreign Exchange Market Sentiment and Exchange Rate Trend

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