An empirical estimation for time and memory algorithm complexities: newly developed R package

Agenis-Nevers, Marc; Bokde, Neeraj Dhanraj; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Shende, Mayur Kishor

doi:10.1007/s11042-020-09471-8

Cited by 14 publications

(6 citation statements)

References 24 publications

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“…We compared the time and space complexities of the models in case studies with the GuessCompx tool. The GuessCompx tool [56,57] empirically estimates the computational complexity of a function in terms of Big-O notations. It computes multiple samples of increasing sizes from the given dataset and estimates the best-fit complexity according to the "leave-one-out mean squared error (LOO-MSE)" approach.…”

Section: Discussionmentioning

confidence: 99%

Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python Package

et al. 2022

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Climate change has contributed substantially to the weather and land characteristic phenomena. Accurate time series forecasting for climate and land parameters is highly essential in the modern era for climatologists. This paper provides a brief introduction to the algorithm and its implementation in Python. The pattern-sequence-based forecasting (PSF) algorithm aims to forecast future values of a univariate time series. The algorithm is divided into two major processes: the clustering of data and prediction. The clustering part includes the selection of an optimum value for the number of clusters and labeling the time series data. The prediction part consists of the selection of a window size and the prediction of future values with reference to past patterns. The package aims to ease the use and implementation of PSF for python users. It provides results similar to the PSF package available in R. Finally, the results of the proposed Python package are compared with results of the PSF and ARIMA methods in R. One of the issues with PSF is that the performance of forecasting result degrades if the time series has positive or negative trends. To overcome this problem difference pattern-sequence-based forecasting (DPSF) was proposed. The Python package also implements the DPSF method. In this method, the time series data are first differenced. Then, the PSF algorithm is applied to this differenced time series. Finally, the original and predicted values are restored by applying the reverse method of the differencing process. The proposed methodology is tested on several complex climate and land processes and its potential is evidenced.

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Section: Discussionmentioning

confidence: 99%

Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python Package

et al. 2022

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“…ese algorithms just need mathematical operators in the update equation along with the best and the worst values. e complexity of JA has been examined empirically in terms of big-O notations using the GuessCompx tool [53,54]. e complexity is compared with the metaheuristic technique (GA) and both methods showed linear complexity (Paliwal et al [36]).…”

Section: Discussionmentioning

confidence: 99%

Proposition of New Metaphor‐Less Algorithms for Reservoir Operation

et al. 2021

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Based on the current water crisis scenario, effective water resources management can play an essential role. Reservoir operation optimization is part of water resources management. Reservoir operation optimization is difficult as it involves a large number of variables and constraints to achieve this goal. The present study aims at exploring the performance of recently developed heuristic algorithms—Rao algorithms as applied to the reservoir operation studies for the first time. Rao algorithms are metaphor-less algorithms that require only basic parameters—population size and function evaluations. In the present study, Rao algorithms have been applied to two case studies: discrete four-reservoir operation system problem and continuous four-reservoir operation system problem (benchmark problems) for the assessment of their performance vis-à-vis other algorithms from the literature. The results showed that the Rao-1 algorithm provided the optimal solution with the least function evaluations when compared to Rao-2, Rao-3, and other algorithms applied in the past to the same benchmark problem. Consequently, the Rao-1 model is found to be superior to these approaches by taking less computational time. Hence, the Rao-1 algorithm can be considered suitable for application to reservoir operation optimization problems.

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“…It follows that the method proposed in this paper is highly demanding from a computational perspective: for instance, the model used here has a memory complexity of 68,093 Mb, while with 12 indicators it would reach a memory complexity of 359,328 Mb. The complexity of the algorithm is estimated using the GuessCompx R package developed by Agenis-Nevers et al (2019). To some extent, the growth of complexity may be thought of as the price of having more general conclusions.…”

Section: The Issue Of Relative Weightsmentioning

confidence: 99%

The probability of multidimensional poverty: A new approach and an empirical application toEU‐SILCdata

Liberati

Resce

Tosi

2022

Review of Income and Wealth

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This paper proposes a novel method to analyze multidimensional poverty by using a large set of feasible weights to summarize the information about the poor, which enables remaining agnostic about the relative importance given to different poverty dimensions. This method allows for the calculation of the individual probability of being poor in a multidimensional perspective. The distribution of individual probabilities can then be combined with Generalized Lorenz dominance techniques to derive unanimous consent for a wide class of social welfare functions with a minimum load of value judgments. The innovations proposed here allow to move from a dual definition of poverty, where poor and non‐poor individuals are classified in a mutually exclusive context, to a continuous measure of deprivation capturing both the extensive and intensive margin of multidimensional poverty. The empirical application of the method consists of measuring multidimensional poverty in ten selected countries using four waves of EU‐SILC data (2008–2014).

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An empirical estimation for time and memory algorithm complexities: newly developed R package

Cited by 14 publications

References 24 publications

Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python Package

Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python Package

Proposition of New Metaphor‐Less Algorithms for Reservoir Operation

The probability of multidimensional poverty: A new approach and an empirical application toEU‐SILCdata

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