Prediction of Wordle based on ARIMA and PLS model
DOI:
https://doi.org/10.54097/9x6q0d69Keywords:
ARIMA Model, PLS Regression, Natural Language Processing, Time series forecastingARIMA Model, PLS Regression, Natural Language Processing, Time series forecasting.Abstract
Nowadays, the New York Times Wordle game gains much popularity in the United States. In this paper, PLS regression analysis is applied to find 5 potential factors to interpret the independent variable information and obtain the prediction distribution result. Extra features such as time series have stationarity and nouns make up the vast majority of the total are investigated through data mining. It is also found that the fluctuating trend of the number of players is consistent with the infectious disease model. The paper gives a prediction of the population of players that is worth reading for reference and a simple classification method of difficulty of words. The model inferred in the paper is expected to help puzzle editors to give a word of proper difficulty.
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