混合SARIMA-LSTM模型用于本地天气预测：一种基于残差学习的数据驱动气象预测方法
Hybrid SARIMA LSTM Model for Local Weather Forecasting: A Residual Learning Approach for Data Driven Meteorological Prediction
AI总结：
本文提出混合SARIMA-LSTM模型，通过残差学习分解温度为气候和天气成分，结合统计稳定性和神经可塑性提升长距离天气预测精度。
摘要：
准确预测长期大气变量仍然是气象科学中的核心挑战，由于大气系统的混沌性质。温度数据代表了确定性循环气候力和随机短期波动的复杂叠加。尽管行星力学驱动可预测的季节性周期性，但快速气象变化如热变异性、气压异常和湿度变化引入了非线性波动，这些波动无法通过简单的外推来处理。历史上，季节性自回归积分滑动平均（SARIMA）模型一直是建模历史天气数据的标准方法，因其能够捕捉线性季节性趋势而受到青睐。然而，SARIMA在平稳性假设下运行，无法捕捉突发的非线性转变。这导致系统性残差误差，表现为对突发尖峰的低估或对下降的过度平滑。相反，深度学习范式，特别是长短期记忆（LSTM）网络，在处理复杂时间序列数据方面表现出卓越的效能。通过利用记忆门，LSTMs学习复杂的非线性依赖关系。然而，LSTMs在开环预测中面临不稳定性；在没有地面真实反馈的情况下，微小偏差会递归地累积，导致发散。为了解决这些限制，我们提出了一种混合SARIMA-LSTM架构。该框架采用残差学习策略将温度分解为可预测的气候成分和非线性天气成分。SARIMA单元建模稳健的长期季节性趋势，而LSTM仅在残差上进行训练，即SARIMA无法捕捉的非线性误差。通过将统计稳定性与神经可塑性相结合，这种混合方法减少了误差传播并提高了长距离准确性。
英文摘要：
Accurately forecasting long-term atmospheric variables remains a defining challenge in meteorological science due to the chaotic nature of atmospheric systems. Temperature data represents a complex superposition of deterministic cyclical climate forces and stochastic, short-term fluctuations. While planetary mechanics drive predictable seasonal periodicities, rapid meteorological changes such as thermal variations, pressure anomalies, and humidity shifts introduce nonlinear volatilities that defy simple extrapolation. Historically, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model has been the standard for modeling historical weather data, prized for capturing linear seasonal trends. However, SARIMA operates under strict assumptions of stationarity, failing to capture abrupt, nonlinear transitions. This leads to systematic residual errors, manifesting as the under-prediction of sudden spikes or the over-smoothing of declines. Conversely, Deep Learning paradigms, specifically Long Short-Term Memory (LSTM) networks, demonstrate exceptional efficacy in handling intricate time-series data. By utilizing memory gates, LSTMs learn complex nonlinear dependencies. Yet, LSTMs face instability in open-loop forecasting; without ground truth feedback, minor deviations compound recursively, causing divergence. To resolve these limitations, we propose a Hybrid SARIMA-LSTM architecture. This framework employs a residual-learning strategy to decompose temperature into a predictable climate component and a nonlinear weather component. The SARIMA unit models the robust, long-term seasonal trend, while the LSTM is trained exclusively on the residuals the nonlinear errors SARIMA fails to capture. By fusing statistical stability with neural plasticity, this hybrid approach minimizes error propagation and enhances long-horizon accuracy.
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