  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.
