融合多种模型的混合框架，提升时间序列预测精度与决策支持能力。
FlowScope: Enhancing Decision Making by Time Series Forecasting based on Prediction Optimization using HybridFlow Forecast Framework
- 结合ARIMA、SARIMA、ETS和LSTM，构建深度混合预测框架
- 在季节性、线性和复杂模式上均表现优异，提升预测准确率
- 适合需要精准长期规划的企业与科研机构
时间序列预测在气象、零售、医疗和金融等领域至关重要，准确预测未来趋势对战略规划和科学决策具有关键作用。本文整合自回归积分滑动平均（ARIMA）在处理线性时间序列上的优势、季节性ARIMA（SARIMA）对季节变化的捕捉能力、指数平滑状态空间模型（ETS）对趋势与误差的建模能力，以及长短期记忆网络（LSTM）在复杂时序关系识别上的优势，提出一种名为FlowScope的深度混合学习框架。该框架通过融合机器学习与深度学习方法，形成一个通用且鲁棒的时间序列预测平台，显著增强企业制定前瞻性决策与优化长期策略的能力。实验验证表明，该框架在多类型数据上具备优越性能。
原文摘要 · Abstract (English)
Time series forecasting is crucial in several sectors, such as meteorology, retail, healthcare, and finance. Accurately forecasting future trends and patterns is crucial for strategic planning and making well-informed decisions. In this case, it is crucial to include many forecasting methodologies. The strengths of Auto-regressive Integrated Moving Average (ARIMA) for linear time series, Seasonal ARIMA models (SARIMA) for seasonal time series, Exponential Smoothing State Space Models (ETS) for handling errors and trends, and Long Short-Term Memory (LSTM) Neural Network model for complex pattern recognition have been combined to create a comprehensive framework called FlowScope. SARIMA excels in capturing seasonal variations, whereas ARIMA ensures effective handling of linear time series. ETS models excel in capturing trends and correcting errors, whereas LSTM networks excel in reflecting intricate temporal connections. By combining these methods from both machine learning and deep learning, we propose a deep-hybrid learning approach FlowScope which offers a versatile and robust platform for predicting time series data. This empowers enterprises to make informed decisions and optimize long-term strategies for maximum performance. Keywords: Time Series Forecasting, HybridFlow Forecast Framework, Deep-Hybrid Learning, Informed Decisions.
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