基于时间序列分析模型的世界数据分析

Xuyi Shi, Jiachen Guang, Liang Shao
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引用次数: 1

摘要

使用LSTM时间序列分析和预测是world游戏发展方向规划和经济收益可视化的重要指导。准确的游戏报告数据预测对于游戏开发、经济投资、赛后规划、提升玩家体验等都具有重要意义。随着《world》游戏变得越来越受欢迎,对游戏的未来做出预测和预测以及整理数据变得至关重要。为了准确预测未来世界选手报告的数据,基于时间序列分析理论,结合对检索数据的广泛收集和筛选,以及LSTM模型和线性回归方程在预测方向上的优势,建立了面向大数据的多维预测模型。利用该预测模型,可以从多个预测维度对世界游戏的发展进行预测。对大数据进行准确预测后,可以分析数据背后的影响因素,在一定程度上可以简化人们对数据的理解,成功实现从尖端技术到服务型需求的转变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wordle data analysis based on time series analysis model
: 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.
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