Wordle game based on time series prediction

Abstract

The wordle game offered daily by the New York Times has taken the United States by storm and is loved by many people and challenged by major internet users. In order to improve the user gaming experience, it is therefore necessary to analyse the game user data. This paper uses a time series model, a random forest regression model, and a random forest classification model, and constructs eight attribute features for the words, predicts the difficulty of guessing EERIE words as being difficult based on the attribute features, and visualises other features of the data. Firstly, the ARIMA(1,1,0) model was used and predicted an approximate prediction interval of [18578, 22562] for the outcome data reported on 1 March 2023. Next, we used a random forest regression model as well as a random forest classification model and predicted the percentage of players who could guess the word EERIE for the first time on March 1, 2023, respectively. The words were categorised by difficulty: easy, average and hard. We finally predicted that the word EERIE belongs to the difficult class. Finally, the following features were created based on the data obtained. The first feature: a time-series plot of the number of reported results; the second feature: a plot of the mean value of the number of attempts from 1 to 7, and the probability of success of the number of attempts by the player is considered to obey a normal distribution. The research in this paper can improve the wordle game to a certain extent, enhance the players' gaming experience and attract more people to participate in the wordle game.