Wavelet Decomposition Impacts on Traditional Forecasting Time Series Models

Shaikh, Wajid Ali; Shah, Syed Feroz; Pandhiani, Siraj Muhammed; Solangi, Muhammad Anwar

doi:10.32604/cmes.2022.017822

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…The improved algorithm, the least-square support vector machine (LSSVM), can fit the data in segments, which allows to reduce the calculation dimensions for data fitting, and save the calculation time and increase the fitting accuracy simultaneously as a result [ 39 , 40 ]. The LSSVM and its improved models [ 41 , 42 ] have been used for traffic flow prediction [ 43 , 44 ], real-time traffic information extraction [ 45 ], importance evaluation of nodes in complex networks [ 46 ], regional risk prediction [ 47 ], and so on. Specially, in the field of metro system, the LSSVM has been applied to predict the time change law of passenger flows [ 32 , 36 , 48 ].…”

Section: Methodsmentioning

confidence: 99%

Deviation of peak hours for metro stations based on least square support vector machine

Yu,

Cui,

Dai

2023

PLoS ONE

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The station-level ridership during the peak hour is one of the key indicators for the design of station size and relevant facilities. However, with the operation of metro system, it cannot be ignored that, in many cities, the station peak and the city peak may not be simultaneously occurred. As the current ridership forecasting methods use the city peak as the point of reference, stations with wide differences of ridership in between would experience disorders due to serious underestimates of passenger demand during the actual peak. Accordingly, this study fully considers the phenomenon that the metro station peak is not identical to the city peak and focuses on the concept of the peak deviation coefficient (PDC), the ratio of the station peak ridership to the city peak ridership. It investigates how metro ridership determinants affects the PDC using the least square support vector machine (LSSVM) model. A land-use function complementarity index is employed as one of the independent variables, which is newly proposed in this study that describes the relationship of the commute land use around an individual station with that along the whole network. This method can help to resolve the ridership amplification indicator for a fine-grained station-level forecasting. The results for Xi’an metro indicate that the LSSVM is an effective method to scrutinize the nonlinear effects of e.g., land use attributes, on the temporal distribution features of the metro ridership. Compared to the ratio of commute land use measured for individual stations, the land-use function complementarity index can better explain and predict the severity of peak deviation phenomenon, controlling other independent variables in the model.

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Section: Methodsmentioning

confidence: 99%

Deviation of peak hours for metro stations based on least square support vector machine

Yu,

Cui,

Dai

2023

PLoS ONE

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“…This is crucial for analyzing nonlinear and non-stationary economic and financial time series, which can interact differently on different time scales [26][27][28][29][30][31][32][33][34][35]. In connection with such undoubted advantages, methods for forecasting nonlinear non-stationary economic and financial time series based on wavelet packet transform and combined methods have recently been actively developed, including Wavelet Artificial Neural Networks (WANN), Wavelet Least-Squares Support Vector Machine (WLSSVM), and Multivariate Adaptive Regression Splines (MARS) [36][37][38][39][40][41][42][43][44][45][46]. Their results indicate a significant increase in the performance and accuracy of traditional time series forecasting models in combination with wavelet packet transform (WPT).…”

Section: Literature Reviewmentioning

confidence: 99%

Improving the Accuracy of Forecasting the TSA Daily Budgetary Fund Balance Based on Wavelet Packet Transforms

Karaev

Gorlova

Sedova

et al. 2022

Journal of Open Innovation: Technology, Market, and Complexity

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Improving the accuracy of cash flow forecasting in the TSA is the key to fulfilling government payment obligations, minimizing the cost of maintaining the cash reserve, providing the absence of outstanding debt accumulation, and ensuring investment in various financial instruments to obtain additional income. The article describes a method for improving the accuracy of forecasting a time series composed of daily budgetary fund balances in the TSA, based on its preliminary decomposition using a discrete wavelet packet transform of the Daubechies family. This makes it possible to increase the accuracy of traditional forecasting methods from 80% to more than 96%. The decomposition level varied from one to eight to minimize the mean absolute error and improve the forecasting accuracy. Calculations of statistical tests for adequacy confirm the effectiveness of the proposed method for improving forecasting accuracy. The scientific novelty of the proposed method for improving the forecasting accuracy of time series from daily budgetary fund balances in the TSA lies in proving the need for preliminary timeseries decomposition and subsequent construction of forecasts for the obtained parts, resulting in high forecasting accuracy. The result differs significantly from traditional econometric methods (ARIMA/SARIMA), characterized by a much lower accuracy (50–80%) and a decrease in forecasting accuracy with an increase in the forecast horizon. This article is novel, as it forms a new approach to solving the problem of increasing the efficiency of using budgetary funds, associated with improving the accuracy of forecasting daily budgetary fund balance in the TSA.

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“…In their paper "Wavelet Decomposition Impacts on Traditional Forecasting Time Series Models", Shaikh et al [9] looked into how wavelet algorithms affect more conventional time-series forecasting models, and it is noted that, on a performance basis, the wavelet combined models are superior to the different time series models.…”

mentioning

confidence: 99%

Introduction to the Special Issue on New Trends in Statistical Computing and Data Science

Chesneau¹,

Doosti²

2023

Computer Modeling in Engineering &Amp; Sciences

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The interface between computer science and statistics has developed considerably in recent years, with exponential progress in the fields of data analysis, stochastic modeling, machine learning, econometrics, simulation, algorithms, classification, and networks. Innovative discoveries in this field appear every day, opening new scientific horizons for the modern world. This is especially true in the post-2020 period, with the treatment of large volumes of data that feed the daily operations of large corporations, as well as the development of artificial intelligence, including advanced machine learning techniques, particularly "deep learning".The aim of the special issue titled "New Trends in Statistical Computing and Data Science" was to publish the most significant articles in these directions, that is, the current progress of statistical computing and data science. Novelty, high quality, and importance are the triptych of the special issue.

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Wavelet Decomposition Impacts on Traditional Forecasting Time Series Models

Cited by 5 publications

References 20 publications

Deviation of peak hours for metro stations based on least square support vector machine

Deviation of peak hours for metro stations based on least square support vector machine

Improving the Accuracy of Forecasting the TSA Daily Budgetary Fund Balance Based on Wavelet Packet Transforms

Introduction to the Special Issue on New Trends in Statistical Computing and Data Science

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