A scalable framework for large time series prediction

Hmamouche, Youssef; Lakhal, Lotfi; Casali, Alain

doi:10.1007/s10115-021-01544-w

Cited by 3 publications

(4 citation statements)

References 32 publications

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“…Time series prediction involves a wide range of fields, including inventory management [13], macroeconomic forecast [14], natural phenomena observations [15], and medical and industrial detection [16]. Highly structured data have strong and complex dependencies among different time steps, and it is a great challenge to effectively model the complex dependencies.…”

Section: Time Series Predictionmentioning

confidence: 99%

A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization

et al. 2023

Systems

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Sales forecasting is a highly practical application of time series prediction. It is used to help enterprises identify and utilize information to reduce costs and maximize profits. For example, in numerous manufacturing enterprises, sales forecasting serves as a key indicator for inventory optimization and directly influences the level of cost savings. However, existing research methods mainly focus on detecting sequences and local correlations from multivariate time series (MTS), but seldom consider modeling the distinct information among the time series within MTS. The prediction accuracy of sales time series is significantly influenced by the dynamic and complex environment, so identifying the distinct signals between different time series within a sales MTS is more important. In order to extract more valuable information from sales series and to enhance the accuracy of sales prediction, we devised a universality–distinction mechanism (UDM) framework that can predict future multi-step sales. Universality represents the instinctive features of sequences and correlation patterns of sales with similar contexts. Distinction corresponds to the fluctuations in a specific time series due to complex or unobserved influencing factors. In the mechanism, a query-sparsity measurement (QSM)-based attention calculation method is proposed to improve the efficiency of the proposed model in processing large-scale sales MTS. In addition, to improve the specific decision-making scenario of inventory optimization and ensure stable accuracy in multi-step prediction, we use a joint Pin-DTW (Pinball loss and Dynamic Time Warping) loss function. Through experiments on the public Cainiao dataset, and via our cooperation with Galanz, we are able to demonstrate the effectiveness and practical value of the model. Compared with the best baseline, the improvements are 57.27%, 50.68%, and 35.26% on the Galanz dataset and 16.58%, 6.07%, and 5.27% on the Cainiao dataset, in terms of the MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Squared Error).

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Section: Time Series Predictionmentioning

confidence: 99%

A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization

et al. 2023

Systems

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“…, support vector regression [ 6 ]), Time series forecasting ( e.g. , stock market forecasting [ 7 ], movie box office prediction [ 8 ], prediction of future [ 9 ] or ongoing epidemic events including COVID-19 [ 10 ], temperature distribution prediction in snap-curing ovens [ 11 ], and multivariate macroeconomic time series prediction [ 12 ]), Partly binary-class or multi-class classification applications ( e.g. , multiple-category water quality classification [ 13 ], multi-class intrusion detection [ 14 ]), and Extreme machine learning applications recently [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Time series forecasting ( e.g. , stock market forecasting [ 7 ], movie box office prediction [ 8 ], prediction of future [ 9 ] or ongoing epidemic events including COVID-19 [ 10 ], temperature distribution prediction in snap-curing ovens [ 11 ], and multivariate macroeconomic time series prediction [ 12 ]),…”

Section: Introductionmentioning

confidence: 99%

BenchMetrics Prob: benchmarking of probabilistic error/loss performance evaluation instruments for binary classification problems

Canbek¹

2023

Int. J. Mach. Learn. & Cyber.

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Probabilistic error/loss performance evaluation instruments that are originally used for regression and time series forecasting are also applied in some binary-class or multi-class classifiers, such as artificial neural networks. This study aims to systematically assess probabilistic instruments for binary classification performance evaluation using a proposed two-stage benchmarking method called BenchMetrics Prob. The method employs five criteria and fourteen simulation cases based on hypothetical classifiers on synthetic datasets. The goal is to reveal specific weaknesses of performance instruments and to identify the most robust instrument in binary classification problems. The BenchMetrics Prob method was tested on 31 instrument/instrument variants, and the results have identified four instruments as the most robust in a binary classification context: Sum Squared Error ( SSE ), Mean Squared Error ( MSE ), Root Mean Squared Error ( RMSE , as the variant of MSE ), and Mean Absolute Error ( MAE ). As SSE has lower interpretability due to its [0, ∞) range, MAE in [0, 1] is the most convenient and robust probabilistic metric for generic purposes. In classification problems where large errors are more important than small errors, RMSE may be a better choice. Additionally, the results showed that instrument variants with summarization functions other than mean ( e.g. , median and geometric mean), LogLoss , and the error instruments with relative/percentage/symmetric-percentage subtypes for regression, such as Mean Absolute Percentage Error ( MAPE ), Symmetric MAPE ( sMAPE ), and Mean Relative Absolute Error ( MRAE ), were less robust and should be avoided. These findings suggest that researchers should employ robust probabilistic metrics when measuring and reporting performance in binary classification problems.

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“…• time-series forecasting (e.g., stock market forecasting [7], movie box office prediction [8], prediction of future [9] or ongoing epidemic events including COVID-19 [10], and multivariate macroeconomic time-series prediction [11]), • partly binary-class or multi-class classification applications (e.g., multiple-category water quality classification [12], multi-class intrusion detection [13]), and • recently extreme machine learning applications [14].…”

Section: Introductionmentioning

confidence: 99%

BenchMetrics Prob: Benchmarking of probabilistic error/loss performance evaluation instruments for binary-classification problems

Canbek¹

2022

Preprint

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Performance evaluation is key to building, training, validating, testing, comparing, and publishing classifier models for machine-learning-based classification problems. Two categories of performance instruments are confusion-matrix-derived metrics such as accuracy, true positive rate, and F1 and graphical-based metrics such as area-under-receiver-operating-characteristic-curve. Probabilistic-based performance instruments that are originally used for regression and time series forecasting are also applied in some binary-class or multi-class classifiers, such as artificial neural networks. Besides widely-known probabilistic instruments such as Mean Squared Error (MSE), Root Mean Square Error (RMSE), and LogLoss, there are many instruments. However, it is not identified that any of those is proper to use specifically in binary-classification performance evaluation. This study proposes BenchMetrics Prob, a qualitative and quantitative benchmarking method, to systematically evaluate probabilistic instruments via five criteria and fourteen simulation cases based on hypothetical classifiers on synthetic datasets. These criteria and cases give more insights to select a proper instrument in a binary-classification performance evaluation. The method was tested on over 31 instruments/instrument variants and the results have distinguished that three instruments are the most robust for binary-classification performance evaluation, namely Sum Squared Error (SSE), MSE with RMSE variant, and Mean Absolute Error (MAE). The results also showed that instrument variants with summarization functions other than mean (e.g., median and geometric mean) and the instrument subtypes proposed later to improve performance evaluation in regression such as relative/percentage/symmetric-percentage error instruments are not robust. Researchers should be aware of using those instruments in selecting or reporting performance in binary classification problems.

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A scalable framework for large time series prediction

Cited by 3 publications

References 32 publications

A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization

A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization

BenchMetrics Prob: benchmarking of probabilistic error/loss performance evaluation instruments for binary classification problems

BenchMetrics Prob: Benchmarking of probabilistic error/loss performance evaluation instruments for binary-classification problems

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