Intelligent Vehicle Sales Prediction Based on Online Public Opinion and Online Search Index

Zhang, Mingyang; Xu, Heyan; Ma, Ning; Pan, Xinglin

doi:10.3390/su141610344

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…Some scholars also apply artificial intelligence algorithms to the problem of automobile sales forecasts. Zhang, et al [22], using the LSTM algorithm, established a smart car sales forecasting model based on the KOL network public opinion and network search index, which improves the forecasting accuracy of smart car sales. Liu, et al [23] proposed a multi-factor sales forecasting model combining discrete wavelet transform and BiLSTM, and obtained the MAE value, Maple value and RMSE value of the optimal DWT-BiLSTM model of 0.811, 5.671 and 1.001, respectively.…”

Section: Prediction Models Of Vehicle Sales Volumementioning

confidence: 99%

Prediction of China Automobile Market Evolution Based on Univariate and Multivariate Perspectives

Dai,

Fang,

Wang

et al. 2023

Systems

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The automobile is an important part of transportation systems. Accurate prediction of sales prospects of different power vehicles can provide an important reference for national scientific decision making, flexible operation of enterprises and rational purchases of consumers. Considering that China has achieved the goal of 20% sales of new energy vehicles ahead of schedule in 2025, in order to accurately judge the competition pattern of new and old kinetic energy vehicles in the future, the automobile market is divided into three types according to power types: traditional fuel vehicles, new energy vehicles and plug-in hybrid vehicles. Based on the monthly sales data of automobiles from March 2016 to March 2023, the prediction effects of multiple models are compared from the perspective of univariate prediction. Secondly, based on the perspective of multivariate prediction, combined with the data of economic, social and technical factors, a multivariate prediction model with high prediction accuracy is selected. On this basis, the sales volume of various power vehicles from April 2023 to December 2025 is predicted. Univariate prediction results show that in 2025, the penetration rates of three types of vehicles will reach 43.8%, 44.4% and 11.8%, respectively, and multivariate prediction results show that the penetration rates will reach 51.0%, 37.9% and 11.1%, respectively.

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Section: Prediction Models Of Vehicle Sales Volumementioning

confidence: 99%

Prediction of China Automobile Market Evolution Based on Univariate and Multivariate Perspectives

Dai,

Fang,

Wang

et al. 2023

Systems

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show abstract

“…Selain itu hal ini juga mengakibatkan pengadaan bahan baku produksi yang tidak sesuai dengan hasil penjualan sehingga dibutuhkan suatu sistem yang dapat memprediksi hasil penjualan bawang goreng sesuai dengan permintaan dan persediaan. Prediksi produksi bawang goreng merupakan salah satu cara untuk menjaga stabilitas penjualan bawang goreng, hasil prediksi yang diperoleh bisa dijadikan pertimbangan untuk mengambil keputusan dalam dalam perencanaan manajemen bisnis, mengembangkan rencanarencana pemasaran, manajemen penjualan, produksi, pembelian, dan logistik guna meningkatkan efisiensi ekonomi serta mengurangi kerugian dalam perencanaan produksi [13], [14]. Penelitian ini menawarkan solusi berupa prediksi produksi bawang goreng bagi UMKM dengan menggunakan metode fuzzy sugeno.…”

Section: Pendahuluanunclassified

Prediksi Tingkat Produksi Bawang Goreng menggunakan Metode K-Means dan Fuzzy Inference System

Wisudawaty

2023

bulletincsr

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Shallots are a strategic commodity because they are needed for household consumption as well as the food industry. Shallots are usually used as a cooking spice, or as a topping for food dishes called fried onions. Shallots are easily damaged, one way to prevent damage is to process shallots into fried onions. Sales of fried onions fluctuate every month due to consumer demand, therefore in this research a grouping of production levels and predictions of fried onion production was carried out. The methods used in this research are K-Means and Fuzzy Sugeno. From the results of research using the K-Means method, there are 3 clusters of fried onion production levels, namely high, medium and small production levels. High production levels were found in months 4, 5, 9, and 10; moderate production levels in months 1, 2, 3, 6, 7, 8 and 11; while a small production level was found in the 12th month. Based on system testing using the fuzzy Sugeno method, data was generated that could be processed and produce 9 rules to serve as a reference in predicting fried onion production for the following years. Apart from that, based on the results of the Mean Absolute Percent Error calculation, the capability of the model created is good and accurate because it has a value of 14.2%. Fried onion production levels in the 4th and 12th months have more accurate predictions compared to other months

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“…Lu et al [45] proposed discovering the dwell position in a user's GPS data and to mine the app usage sequence using the sequence algorithm to predict the next app to be used. Some studies indicate a user's subsequent interaction behavior through time, location, and the app last used [46][47][48]. Zemla et al [49] predicted users' revisitation behaviors by the frequency with which they accessed apps, as well as the users' contexts.…”

Section: Mobile Search Behavior Pathsmentioning

confidence: 99%

Understanding the Paths and Patterns of App-Switching Experiences in Mobile Searches

Liang

Dong

2022

Sustainability

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Mobile searches have become the main channel for people to search for information, and mobile searches have received attention in the field of information-seeking behavior. Especially as users use various apps to search network information, the app-switching behaviors in mobile searches have also attracted scholars’ attention in recent years. Research on app-switching behaviors in mobile searches can help to further understand users’ search motivations, evaluate search results, and improve users’ mobile search experiences. This study recruited participants (n = 30) and conducted a 15-day user experiment. This study collected all participants’ mobile phone log data during the experiment and identified the app-switching behaviors in mobile searches through a log collection tool. This study aimed to discover the app-switching behavior paths and patterns in mobile searches. Firstly, the basic characteristics of app-switching behaviors in mobile searches were analyzed, as were the app-switching paths in mobile searches from the perspective of switching probability between apps. Then, the different behaviors in mobile search sessions were identified and app-switching behavior patterns were put forward. These behavior patterns summarize user behavior changes in mobile search sessions. This paper focused on analyzing app-switching behavior paths under different patterns and found apparent differences in app-switching behavior paths. This study examined mobile search behavior from the perspective of app-switching. The research of this paper can help to better understand the relationship between users’ mobile search behaviors and app interactions and is an excellent supplement to the analysis of mobile search behaviors.

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Intelligent Vehicle Sales Prediction Based on Online Public Opinion and Online Search Index

Cited by 8 publications

References 33 publications

Prediction of China Automobile Market Evolution Based on Univariate and Multivariate Perspectives

Prediction of China Automobile Market Evolution Based on Univariate and Multivariate Perspectives

Prediksi Tingkat Produksi Bawang Goreng menggunakan Metode K-Means dan Fuzzy Inference System

Understanding the Paths and Patterns of App-Switching Experiences in Mobile Searches

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