Wordle User Prediction and Analysis Based on SARIMA Algorithm, LSTM Recurrent Neural Network, and XGBoost Model
文献类型： 会议论文
第一作者： Muhao Shi
作者： Muhao Shi 1 ; Yilong Huang 1 ; Lejian Zhang 1 ; Zetong Jin 2 ; Yuhan Zhang 3 ;
作者机构： 1.College of Physics, Sichuan University, Chengdu, China
2.College of Biomass Science and Engineering, Sichuan University, Chengdu, China
3.School of Cyber Science and Engineering, Sichuan University, Chengdu, China
关键词： Analytical models;Recurrent neural networks;Mechatronics;Time series analysis;Games;Machine learning;Predictive models
会议名称： International Conference on Mechatronics, IoT and Industrial Informatics
主办单位：
页码： 616-620
摘要： This paper presents an analysis of Wordle user prediction and analysis using the SARIMA algorithm, LSTM recurrent neural network, and XGBoost model. Firstly, the SARIMA algorithm is employed for time series analysis, predicting the future changes in the number of Wordle game participants based on the 2022 data. The model forecasts that the number of Wordle game participants on March 1, 2023, will be 16854.200060080555, with a corresponding prediction interval of ±13883.579317580654. Subsequently, text feature vectorization and other methods are utilized to classify words based on different attributes such as letter frequency, repetition count, Grade Level, Word Rank, and Syllable Count. Furthermore, LSTM and XGBoost models are separately employed to train the relationship between different word attributes and seven different scoring proportions, predicting future scoring situations based on the respective models. The final predicted conditions for the word "EERIE" on March 1, 2023, are as follows: LSTM: {0.0000, 13.4734, 19.0247, 40.0381, 18.9592, 7.1761, 1.3284}, XGBoost: {1.0349, 8.5148, 28.5024, 28.5481, 19.6343, 9.0262, 1.6482} (rounded to four decimal places). The average 7% RMSE for both models is 1.0073 and 4.9797, indicating high reliability.
分类号： th15`tp3
- 相关文献
[1]Long Image Time Series for Crop Extraction Based on the Automatically Generated Samples Algorithm. Nan Jia,Yinshuai Li,Andrés Vi?a,Jie Cheng,Yue Dou,Qian Song,Liuyue He,Jianguo Liu. 2023
[2]Validation and Analysis of Long-Term AATSR Land Surface Temperature Product in the Heihe River Basin, China. Ouyang, Xiaoying,Lei, Yonghui,Hu, Guangcheng,Ouyang, Xiaoying,Chen, Dongmei,Duan, Si-Bo,Lei, Yonghui,Dou, Youjun. 2017
[3]Time-series modeling and prediction of global monthly absolute temperature for environmental decision making. Ye Liming,Yang Guixia,Ye Liming,Van Ranst, Eric,Tang Huajun. 2013
[4]Short-Term Price Forecasting For Agro-products Using Artificial Neural Networks. Li, Gan-qiong,Xu, Shi-wei,Li, Zhe-min. 2010
[5]Using Machine Learning in Environmental Tax Reform Assessment for Sustainable Development: A Case Study of Hubei Province, China. Zheng, Yinger,Zheng, Yinger,Zheng, Haixia,Zheng, Haixia,Ye, Xinyue. 2016
[6]Determination of internal qualities of Newhall navel oranges based on NIR spectroscopy using machine learning. Liu, Cong,Yang, Simon X.,Liu, Cong,Deng, Lie.
[7]Land use and land cover change in Inner Mongolia - understanding the effects of China's re-vegetation programs. Yin, He,Pflugmacher, Dirk,Hostert, Patrick,Li, Ang,Li, Zhengguo,Hostert, Patrick. 2018
[8]A comparative study for least angle regression on NIR spectra analysis to determine internal qualities of navel oranges. Liu, Cong,Yang, Simon X.,Liu, Cong,Deng, Lie. 2015
[9]Design and Testing of Transplanting Hole-Forming Machine for Potted Rapeseed Seedlings. Xiang Wei,Wu Mingliang,Lyu Jiangnan,Ma Lan,Quan Wei,Liu Jiajie. 2019
[10]Human-Exoskeleton Misalignment Reduction on Knee Joint via an RPR Mechanism-Based Device. Cunxi Dai,Peiwen Fu,Bin Zhong,Kaiqi Guo,Mingming Zhang. 2022
[11]Translating Hungarian language dialects using natural language processing models. Barbara Simon,ádám Hartveg,Lehel Dénes-Fazakas,Gy?rgy Eigner,László Szilágyi. 2023
[12]Development of Gaming AI: A Case Study. Zsolt Péter Takács,Miklós Vincze,György Eigner. 2024
[13]Assessing the Potential of Brain-Computer Interface Multiplayer Video Games using c-VEPs: A Pilot Study. Selene Moreno-Calderón,Víctor Martínez-Cagigal,Eduardo Santamaría-Vázquez,Sergio Pérez-Velasco,Diego Marcos-Martínez,Roberto Hornero. 2023
[14]Deep Learning based sEMG decoding for TelePresence device control. ádám Gyula Nemes,Levente Kovács,Gy?rgy Eigner,Gergely Márton,Béla Weiss. 2023
[15]Short-Term Price Forecasting For Agro-products Using Artificial Neural Networks. Gan-qiong Li,Shi-wei Xu,Zhe-inin Li. 2010
[16]Mapping Rice Area Using Sentinel-1 SAR Data and Deep Learning. Guozhuang Shen,Chenwei Nie. 2023
[17]ECG Morphology-Based Markers for Risk Stratification in Hypertrophic Cardiomyopathy. Inés Noguero-Soler,Javier Ramos-Maqueda,Pablo Revilla-Martí,Esther Pueyo,Ana Mincholé. 2023
[18]Classification of ischemic and dilated cardiomyopathy patients based on the analysis of the pulse transit time. Beatriz F. Giraldo,Javier Rodriguez,Steffen Schulz,Andreas Voss. 2021
[19]Respiratory Pattern Analysis for Different Breathing Types and Recording Sensors in Healthy Subjects. Jordi Solà-Soler,Daniel Romero Pérez,Leon Balchin,Arantxa Mas Serra,Manel Lujan Torné,Melinda R. Popoviciu Koborzan,Beatriz F. Giraldo Giraldo. 2023
[20]Adaptive responses of cancer cells to tumor microenvironment changes: an evolutionary model perspective. Chiara Romano,Alessandro Borri,Maria Domenica Di Benedetto. 2024
