Analysis and Prediction of Wordle Dataset Based on ARIMA and Pearson's Correlation Coefficient

Zixin Wan

doi:10.54097/fcis.v3i2.7082

Authors

Zixin Wan

DOI:

https://doi.org/10.54097/fcis.v3i2.7082

Keywords:

ARIMA, Pearson's correlation coefficient, Chi-square test

Abstract

In this paper, a time series analysis was performed to ensure the parameters p, d, and q. Finally, the ARIMA (1,1,0) model was chosen to create a prediction interval for the number of outcomes to be reported in the future. The results of the precision test showed good confidence in the prediction with a goodness-of-fit of 98.2%. the prediction interval for March 1, 2023 was [10103,10523]. In addition, this paper used Pearson's correlation coefficient and chi-square test to verify the correlation between each attribute of the word and the percentage of scores in the hard model. In summary, this paper found that the attributes of words definitely affect the percentage of scores, and these scores are mainly the initials and lexicality of words.

Downloads

Download data is not yet available.

References

Zeng B, Tong M, Ma X. A new-structure grey Verhulst model: development and performance comparison[J]. Applied Mathematical Modelling, 2020, 81: 522-537.

Wang Z, Dang Y, Liu S. Unbiased grey Verhulst model and its application[J]. Systems Engineering-Theory & Practice, 2009, 29(10): 138-144.

Ariyo A A, Adewumi A O, Ayo C K. Stock price prediction using the ARIMA model[C]//2014 UKSim-AMSS 16th international conference on computer modelling and simulation. IEEE, 2014: 106-112.

Wang Z X, Li Q. Modelling the nonlinear relationship between CO2 emissions and economic growth using a PSO algorithm-based grey Verhulst model[J]. Journal of Cleaner Production, 2019, 207: 214-224.

Zeng B, Ma X, Zhou M. A new-structure grey Verhulst model for China’s tight gas production forecasting[J]. Applied Soft Computing, 2020, 96: 106600.

Tang L, Lu Y. Study of the grey Verhulst model based on the weighted least square method[J]. Physica A: Statistical Mechanics and its Applications, 2020, 545: 123615.

Hillmer S C, Tiao G C. An ARIMA-model-based approach to seasonal adjustment[J]. Journal of the American Statistical Association, 1982, 77(377): 63-70.

Sowell F. Modeling long-run behavior with the fractional ARIMA model[J]. Journal of monetary economics, 1992, 29(2): 277-302.

Fattah J, Ezzine L, Aman Z, et al. Forecasting of demand using ARIMA model[J]. International Journal of Engineering Business Management, 2018, 10: 1847979018808673.

Nochai R, Nochai T. ARIMA model for forecasting oil palm price[C]//Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and applications. 2006: 13-15.