Shi, L., Chen, Y., Lin, J., et al. (2023) A Black-Box Model for Predicting Difficulty of Word Puzzle Games: A Case Study of Wordle. Knowledge and Information Systems, 66, 1729–1750.
https://doi.org/10.1007/s10115-023-01992-6

has been cited by the following article:

• TITLE: Prediction of Wordle Scores Based on ARIMA and LSTM Models

  AUTHORS: Biyun Chen, Wenqiang Li

  KEYWORDS: Time Series, ARIMA, LSTM, Wordle, Prediction

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.12 No.2, February 29, 2024

  ABSTRACT: This paper examines the effectiveness of the Differential autoregressive integrated moving average (ARIMA) model in comparison to the Long Short Term Memory (LSTM) neural network model for predicting Wordle user-reported scores. The ARIMA and LSTM models were trained using Wordle data from Twitter between 7th January 2022 and 31st December 2022. User-reported scores were predicted using evaluation metrics such as MSE, RMSE, R2, and MAE. Various regression models, including XG-Boost and Random Forest, were used to conduct comparison experiments. The MSE, RMSE, R2, and MAE values for the ARIMA(0,1,1) and LSTM models are 0.000, 0.010, 0.998, and 0.006, and 0.000, 0.024, 0.987, and 0.013, respectively. The results indicate that the ARIMA model is more suitable for predicting Wordle user scores than the LSTM model.