Wordle data analysis based on time series analysis model

Abstract

Using LSTM time series analysis and forecasting is an important guide for Wordle's game development direction planning and economic revenue visualization. Accurate game report data prediction is of great significance for game development, economic investment, post-game planning, and improving player experience. As Wordle's game becomes more and more popular, it is essential to make predictions and projections about the future of the game as well as collate the data. In order to accurately predict the data reported by Wordle players in the future, based on the theory of time series analysis, combined with the extensive collection and screening of retrieval data, and the advantages of LSTM model and linear regression equation in the direction of prediction, a multi-dimensional prediction model for big data was established. With this prediction model, the development of Wordle games can be predicted according to a variety of prediction dimensions. After the accurate prediction of big data, the influential factors behind the data can be analyzed, which can simplify people's understanding of data to a certain extent, and successfully realize the transition from sophisticated technology to service-oriented demand.