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：在本研究中，我们聚焦于开发并实现一个综合性的数值时间序列预测模型集成，统称为“数值时间序列预测模型组”（G-NM）。该集成涵盖传统模型，包括自回归积分滑动平均模型（ARIMA）、Holt-Winters方法及支持向量回归（SVR），以及现代神经网络模型，如循环神经网络（RNN）和长短期记忆网络（LSTM）。G-NM专为增强我们对复杂自然现象中固有模式与趋势的预测能力而构建。通过利用与这些现象相关的时间序列数据，G-NM能够支持对上述现象的长期预测。本研究的主要目标既包括深化我们对这些事件的理解，也在于显著提升预测的准确性。G-NM同时捕捉时间序列数据中的线性与非线性依赖关系、季节性和趋势。这些模型各有独特优势：ARIMA在处理线性趋势与季节性方面具有稳健性，SVR擅长捕获非线性模式，而LSTM则能灵活地对时间序列数据的各种成分进行建模。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前沿进展。我们预期，这项研究将成为我们持续探索和理解自然界复杂事件征程中的一块重要基石。