Discrete Chi-Square Method discovers solar forcing in El Niño time series: The Pacific Ocean as a bolometer measuring the solar dynamo light curve in real-time
离散卡方方法与厄尔尼诺时间序列中的太阳强迫:太平洋辐射计解释AI 解读 ↓
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离散卡方方法与厄尔尼诺时间序列中的太阳强迫:太平洋辐射计解释AI 解读 ↓
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Discrete Chi-square Method (DCM) can detect multiple signals superimposed on an arbitrary trend. DCM's backbone is Gauss-Markov theorem that Least Squares (LS) is the best unbiased estimator for linear regression models. DCM is robust because it computes numerous linear model LS fits. Discrete Fourier Transform and other frequency-domain methods have many application limitations. None of those limitations constrains DCM. Fisher-test provides signal significances and identifies the best DCM model, which is validated by Forecast-test. Simulations verify the Window Dimension Effect (WD-effect): "For any sample window $ΔT$, DCM inevitably detects the correct $p(t)$ trend and $h(t)$ signal(-s) when sample size $n$ and/or data accuracy $σ$ increase". WD-effect "sees through time". DCM's model analytical solution is ill-posed. We present a computational well-posed solution. Mainstream considers El Niño phenomenon chaotic. Usual forecasts are probabilistic, not deterministic. We use El Niño time series to stress-test DCM. It detects the multi-periodic "Big wave" superimposed on global warming trend. This gives accurate El Niño forecasts. Our real-time forecast outperforms those of official agencies. Only solar forcing, not chaotic ocean-atmosphere coupling, can cause the "Big wave" cooling the Pacific Ocean at sunspot minima. The ocean acts like a giant bolometer measuring the deterministic "Solar dynamo light curve". DCM detects multi-periodicity in the ocean and sunspot record. The mainstream stochastic dynamo cannot cause this. Planetary tidal forces may drive solar dynamo. Future El Niño models must integrate astrophysical cycles with chaotic climatological fluid dynamics. Validating our analysis now can save trillions (USD) in El Niño damages.
基于分位数的 Kumaraswamy–Teissier 自回归移动平均模型
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WIRED:结构化依赖下的加权自适应多序列概率预测
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加性矩阵整数值自回归模型
Contemporary data-driven and technology-integrated era, various matrix-valued integer-valued time series, such as cross-regional crime statistics, multi-category sales records, and network traffic matrices, exhibit high dimensionality, complex structures, and strong row-column intertwined dependencies. Although the existing matrix integer-valued autoregressive (MINAR) model provides a framework that directly handles matrix data and captures bidirectional row-column…