In this study, we focus on the development and implementation of a comprehensive ensemble of numerical time series forecasting models, collectively referred to as the Group of Numerical Time Series Prediction Model (G-NM). This inclusive set comprises traditional models such as Autoregressive Integrated Moving Average (ARIMA), Holt-Winters' method, and Support Vector Regression (SVR), in addition to modern neural network models including Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). G-NM is explicitly constructed to augment our predictive capabilities related to patterns and trends inherent in complex natural phenomena. By utilizing time series data relevant to these events, G-NM facilitates the prediction of such phenomena over extended periods. The primary objective of this research is to both advance our understanding of such occurrences and to significantly enhance the accuracy of our forecasts. G-NM encapsulates both linear and non-linear dependencies, seasonalities, and trends present in time series data. Each of these models contributes distinct strengths, from ARIMA's resilience in handling linear trends and seasonality, SVR's proficiency in capturing non-linear patterns, to LSTM's adaptability in modeling various components of time series data. Through the exploitation of the G-NM potential, we strive to advance the state-of-the-art in large-scale time series forecasting models. We anticipate that this research will represent a significant stepping stone in our ongoing endeavor to comprehend and forecast the complex events that constitute the natural world.

翻译：本研究聚焦于开发与实现一套综合性的数值时间序列预测模型集合，统称为“数值时间序列预测模型组”（G-NM）。该集合包含自回归积分滑动平均模型（ARIMA）、霍尔特-温特斯方法、支持向量回归（SVR）等传统模型，以及循环神经网络（RNN）和长短期记忆网络（LSTM）等现代神经网络模型。G-NM的构建旨在增强我们对复杂自然现象中固有模式与趋势的预测能力。通过利用与这些事件相关的时间序列数据，G-NM可实现对此类现象的长期预测。本研究的主要目标既在于深化对这些现象的理解，也在于显著提升预测精度。G-NM同时捕获时间序列数据中的线性与非线性依赖关系、季节性与趋势。各模型贡献独特优势：ARIMA擅长处理线性趋势与季节性，SVR精于捕捉非线性模式，LSTM则能灵活建模时间序列数据的多种成分。通过挖掘G-NM的潜力，我们致力于推动大规模时间序列预测模型的发展水平。我们预期本研究将在持续探索和理解构成自然界的复杂事件的征程中，成为重要的里程碑式进展。