Welcome to Francis Academic Press

Academic Journal of Mathematical Sciences, 2023, 4(2); doi: 10.25236/AJMS.2023.040208.

Wordle data analysis based on time series analysis model


Xuyi Shi, Jiachen Guang, Liangsu Shao

Corresponding Author:
Xuyi Shi

School of Mathematics and Statistics, Beijing Technology and Business University, Beijing, China, 100048


Using LSTM time series analysis and forecasting is an important guide for Wordle's game development direction planning and economic revenue visualization. Accurate game report data prediction is of great significance for game development, economic investment, post-game planning, and improving player experience. As Wordle's game becomes more and more popular, it is essential to make predictions and projections about the future of the game as well as collate the data. In order to accurately predict the data reported by Wordle players in the future, based on the theory of time series analysis, combined with the extensive collection and screening of retrieval data, and the advantages of LSTM model and linear regression equation in the direction of prediction, a multi-dimensional prediction model for big data was established. With this prediction model, the development of Wordle games can be predicted according to a variety of prediction dimensions. After the accurate prediction of big data, the influential factors behind the data can be analyzed, which can simplify people's understanding of data to a certain extent, and successfully realize the transition from sophisticated technology to service-oriented demand.


Machine learning, Time series analysis, Linear regression

Cite This Paper

Xuyi Shi, Jiachen Guang, Liangsu Shao. Wordle data analysis based on time series analysis model. Academic Journal of Mathematical Sciences (2023) Vol. 4, Issue 2: 53-59. https://doi.org/10.25236/AJMS.2023.040208.


[1] Zhou Yehan. Research on Time Series Analysis Based on Deep Learning and its Application in Data Center [D]. Nanjing University of Posts and Telecommunications, 2022:1-65.

[2] Brochwell P.J, Davis R.A, Berger J.O, et al. Time Series: Theory and Methods [M]. Berlin, Springer-Verlag, 2015: 2-35.

[3] Jia Mingzhu. Research and Application of Time Series Analysis Method based on Machine Learning [D]. Xi 'an University of Science and Technology, 2020:1-77. 

[4] Yang Yujun. Research on Time Series Model Based on Machine Learning and its application [D]. University of Electronic Science and Technology of China,2018:1-116 

[5] Jiao Zinan, Chen Nian, Jin Tao, Wang Jianmin. Anomaly detection of Industrial Internet Time Series based on Spectral Residual method [J/OL]. Computer Integrated Manufacturing Systems, 2023: 1-21.

[6] Singh P, Dwivedi P, Kant V. A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting [J]. Energy, 2019, 174(1): 460-477.

[7] Yuan Ximin, Huang Yuqi, Tian Fuchang, Cao Lugan. Prediction method of storm surge Water Increase based on LSTM-GM neural network model [J/OL]. Water Resources Conservation,2023:1-12 

[8] Wu Jianping, Hou Ke. A comparative study of English Synonyms based on COCA Corpus [J]. Expo of Chinese Nationalities,2020,188(16):93-94 

[9] Li Xiaohan. Analysis of the relationship between Marine economy development and transportation in China based on multiple linear regression analysis model. Transportation Energy Conservation and Environmental Protection,2023:1-7

[10] Shuai Wang, Yufu Ning, Hongmei shi. A new uncertain linear regression model based on equation deformation [J]. Soft Comput, 2021, 25(20): 12817-12824.

[11] Zhang Hanxia. Scenario analysis applicable to linear regression and logistic regression [J]. Automation & Instrumentation, 2022, 276(10):1-4+8.