Mixed-frequency data-driven forecasting the important economies' performance in a smart city: a novel RUMIDAS-SVR model

Wang, Weiqing; Zhang, Zengbin; Wang, Liukai; Zhang, Xiaobo; Zhang, Zhenyu

doi:10.1108/imds-01-2022-0014

Cited by 3 publications

(1 citation statement)

References 79 publications

(151 reference statements)

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“…Unlike the conventional MIDAS model, whose frequency of the dependent variable cannot be higher than the lowest frequency of explanatory variables, the RMIDAS method can directly utilize explanatory variables with a lower frequency than the dependent variable. Other studies have also confirmed the usefulness of the RMIDAS model in several fields: Xu et al (2021) used quarterly GDP and monthly inflation to predict US daily interest rates through the RMIDAS model, Wang et al (2022) forecasted weekly market value of technology-listed companies using monthly indicators by combining the support vector regression (SVR) and the unrestricted RMIDAS (RUMIDAS) model, and Foroni et al (2023) introduced a Bayesian approach to the RUMIDAS model and forecasted daily electricity prices using monthly macroeconomic information. Although adopted in many ways, the RMIDAS model has not been used in tourism demand forecasting, and its performance in this field has not yet been investigated.…”

Section: Tourism Demand Forecasting With Mixed-frequency Modelsmentioning

confidence: 97%

Tourism Demand Forecasting With Multiple Mixed-Frequency Data: A Reverse Mixed-Data Sampling Method

Wu,

Li,

Wen

et al. 2023

Journal of Travel Research

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Due to the limitations of existing tourism demand forecasting models, data with frequencies lower than those of the tourism demand need to be processed in advance and cannot be directly used in a model, which leads to the loss of timeliness and accuracy in tourism demand forecasting. Taking the inbound tourism of the United States prior to and during the COVID-19 pandemic as an example, this study systematically examines the impact of data frequency processing on tourism demand modeling and forecasting, through the construction and comparison of three categories of models, with a particular focus on the first developed multiple mixed-frequency specification of reverse mixed-data sampling (RMIDAS) model. The results confirm the positive effect of multiple mixed-frequency models, which can directly use various original data frequencies, in improving the accuracy of tourism demand forecasting. This study also provides important guidance for future research on high-frequency tourism variables forecasting.

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Section: Tourism Demand Forecasting With Mixed-frequency Modelsmentioning

confidence: 97%

Tourism Demand Forecasting With Multiple Mixed-Frequency Data: A Reverse Mixed-Data Sampling Method

Wu,

Li,

Wen

et al. 2023

Journal of Travel Research

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Data-driven optimization for production planning with multiple demand features

Su,

Zeng,

Shao

et al. 2023

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PurposeThe production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.Design/methodology/approachIn this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.FindingsNumerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.Originality/valueCompared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

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Development of a Time Series E-Commerce Sales Prediction Method for Short-Shelf-Life Products Using GRU-LightGBM

Chen,

Xie,

Pei

et al. 2024

Applied Sciences

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Effective sales prediction for e-commerce would assist retailers in developing accurate production and inventory control plans, which would further help them to reduce inventory costs and overdue losses. This paper develops a systematic method for e-commerce sales prediction, with a particular focus on predicting the sales of products with short shelf lives. The short-shelf-life product sales prediction problem is poorly addressed in the existing literature. Unlike products with long shelf lives, short-shelf-life products such as fresh milk exhibit significant fluctuations in sales volume and incur high inventory costs. Therefore, accurate prediction is crucial for short-shelf-life products. To solve these issues, a stacking method for prediction is developed based on the integration of GRU and LightGBM. The proposed method not only inherits the ability of the GRU model to capture timing features accurately but also acquires the ability of LightGBM to solve multivariable problems. A case study is applied to examine the accuracy and efficiency of the GRU-LightGBM model. Comparisons among other sales prediction methods such as ARIMA and SVR are also presented. The comparative results show that the GRU-LightGBM model is able to predict the sales of short-shelf-life products with higher accuracy and efficiency. The selected features of the GRU-LightGBM model are also useful due to their interpretability while developing sales strategies.

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Mixed-frequency data-driven forecasting the important economies' performance in a smart city: a novel RUMIDAS-SVR model

Cited by 3 publications

References 79 publications

Tourism Demand Forecasting With Multiple Mixed-Frequency Data: A Reverse Mixed-Data Sampling Method

Tourism Demand Forecasting With Multiple Mixed-Frequency Data: A Reverse Mixed-Data Sampling Method

Data-driven optimization for production planning with multiple demand features

Development of a Time Series E-Commerce Sales Prediction Method for Short-Shelf-Life Products Using GRU-LightGBM

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