标题： 一种时间序列预测的新型混合方法：使用无监督学习和样条插值进行周期估计和气候数据分析 显示英文标题

标题： A Novel Hybrid Approach for Time Series Forecasting: Period Estimation and Climate Data Analysis Using Unsupervised Learning and Spline Interpolation

摘要： 本文探讨了一种用于查恩奈气候数据的时间序列预测的新方法。 我们的方法包括两种不同的已建立的时间序列模型，利用它们在处理季节性和周期性方面的优势。 值得注意的是，开发了一种新算法，使用无监督机器学习和样条插值技术来计算时间序列的周期。 通过一个细致的集成过程，将这两种模型结合起来，我们实现了优化的预测。 这项研究有助于推进预测技术，并为气候数据分析提供了有价值的见解。 显示英文摘要

摘要： This article explores a novel approach to time series forecasting applied to the context of Chennai's climate data. Our methodology comprises two distinct established time series models, leveraging their strengths in handling seasonality and periods. Notably, a new algorithm is developed to compute the period of the time series using unsupervised machine learning and spline interpolation techniques. Through a meticulous ensembling process that combines these two models, we achieve optimized forecasts. This research contributes to advancing forecasting techniques and offers valuable insights into climate data analysis.