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非线性结构 VAR 中的共同趋势与长期识别AI 解读

While it is widely recognised that linear (structural) VARs may fail to capture important aspects of economic time series, the use of nonlinear SVARs has to date been almost entirely confined to the modelling of stationary time series, because of a lack of understanding as to how common stochastic trends may be accommodated within nonlinear models. This has unfortunately circumscribed the range of series to which such models can be applied -- and/or required that these…

随机几何对象时间序列的形状分析统计框架AI 解读

We introduce a new framework to analyze shape descriptors that capture the geometric features of an ensemble of point clouds. At the core of our approach is the point of view that the data arises as sampled recordings from a metric space-valued stochastic process, possibly of nonstationary nature, thereby integrating geometric data analysis into the realm of functional time series analysis. Our framework allows for natural incorporation of spatial-temporal dynamics,…

利用高斯过程处理混频数据的即时预测AI 解读

We develop Bayesian machine learning methods for mixed data sampling (MIDAS) regressions. This involves handling frequency mismatches and specifying functional relationships between many predictors and the dependent variable. We use Gaussian processes (GPs) and compress the input space with structured and unstructured MIDAS variants. This yields several versions of GP-MIDAS with distinct properties and implications, which we evaluate in short-horizon now- and forecasting…

利用高斯过程上的随机游走预测用电量AI 解读

We consider time-series forecasting problems where data is scarce, difficult to gather, or induces a prohibitive computational cost. As a first attempt, we focus on short-term electricity consumption in France, which is of strategic importance for energy suppliers and public stakeholders. The complexity of this problem and the many levels of geospatial granularity motivate the use of an ensemble of Gaussian Processes (GPs). Whilst GPs are remarkable predictors, they are…

偏最小二乘法令人意外的稳健表现AI 解读

Partial least squares (PLS) is a simple factorisation method that works well with high dimensional problems in which the number of observations is limited given the number of independent variables. In this article, we show that PLS can perform better than ordinary least squares (OLS), least absolute shrinkage and selection operator (LASSO) and ridge regression in forecasting quarterly gross domestic product (GDP) growth, covering the period from 2000 to 2023. In fact,…

复杂时间动态下 M 回归的自卷积 BootstrapAI 解读

The paper considers simultaneous nonparametric inference for a wide class of M-regression models with time-varying coefficients. The covariates and errors of the regression model are tackled as a general class of nonstationary time series and are allowed to be cross-dependent. A novel and easy-to-implement self-convolved bootstrap procedure is proposed. With only one tuning parameter, the bootstrap facilitates a $\sqrt{n}$-consistent inference of the cumulative regression…

删失数据预测：结合时间聚合的 Tobit 指数平滑AI 解读

This study introduces a novel approach to forecasting by Tobit Exponential Smoothing with time aggregation constraints. This model, a particular case of the Tobit Innovations State Space system, handles censored observed time series effectively, such as sales data, with known and potentially variable censoring levels over time. The paper provides a comprehensive analysis of the model structure, including its representation in system equations and the optimal recursive…

具有协整关系的分数阶单整曲线时间序列AI 解读

We introduce methods and theory for fractionally cointegrated curve time series. We develop a variance-ratio test to determine the dimensions associated with the nonstationary and stationary subspaces. For each subspace, we apply a local Whittle estimator to estimate the long-memory parameter and establish its consistency. A Monte Carlo study of finite-sample performance is included, along with two empirical applications.

通过聚类构建层级时间序列：是否存在最优预测方式？AI 解读

Forecast reconciliation has attracted significant research interest in recent years, with most studies taking the hierarchy of time series as given. We extend existing work that uses time series clustering to construct hierarchies, with the goal of improving forecast accuracy, in three ways. First, we investigate multiple approaches to clustering, including not only different clustering algorithms, but also the way time series are represented and how distance between time…

空间自回归模型的迁移学习及美国总统选举预测应用AI 解读

It is important to incorporate spatial geographic information into U.S. presidential election analysis, especially for swing states. The state-level analysis also faces significant challenges of limited spatial data availability. To address the challenges of spatial dependence and small sample sizes in predicting U.S. presidential election results using spatially dependent data, we propose a novel transfer learning framework within the SAR model, called as tranSAR. Classical…

非平稳自回归过程精度矩阵的边缘特征值AI 解读

This paper investigates structural changes in the parameters of first-order autoregressive models by analyzing the edge eigenvalues of the precision matrices. Specifically, edge eigenvalues in the precision matrix are observed if and only if there is a structural change in the autoregressive coefficients. We show that these edge eigenvalues correspond to the zeros of a determinantal equation. Additionally, we propose a consistent estimator for detecting outliers within the…

异质性时间序列的惩罚化子群划分AI 解读

Interest in the study and analysis of dynamic processes in the social, behavioral, and health sciences has burgeoned in recent years due to the increased availability of intensive longitudinal data. However, how best to model and account for the persistent heterogeneity characterizing such processes remains an open question. The multi-VAR framework, a recent methodological development built on the vector autoregressive model, accommodates heterogeneous dynamics in…

混合效应随机微分方程的似然估计AI 解读

Stochastic differential equations provide a powerful tool for modelling dynamic phenomena affected by random noise. In case of repeated observations of time series for several experimental units, it is often the case that some of the parameters vary between the individual experimental units, which has motivated a considerable interest in stochastic differential equations with mixed effects, where a subset of the parameters are random. These models enable simultaneous…

可视化分布差异：形式化的多样本图形检验AI 解读

Classical tests are available for the two-sample test of correspondence of distribution functions. From these, the Kolmogorov-Smirnov test provides also the graphical interpretation of the test results, in different forms. Here, we propose modifications of the Kolmogorov-Smirnov test with higher power. The proposed tests are based on the so-called global envelope test which allows for graphical interpretation, similarly as the Kolmogorov-Smirnov test. The tests are based on…

存在相关市场微观结构噪声时的已实现波动率状态空间模型AI 解读

Volatility means the degree of variation of a stock price which is important in finance. Realized Volatility (RV) is an estimator of the volatility calculated using high-frequency observed prices. RV has lately attracted considerable attention of econometrics and mathematical finance. However, it is known that high-frequency data includes observation errors called market microstructure noise (MN). Nagakura and Watanabe[2015] proposed a state space model that resolves RV into…

总体规划社区增长动态的建模AI 解读

This paper describes how a time-varying Markov model was used to forecast housing development at a master-planned community during a transition from high to low growth. Our approach draws on detailed historical data to model the dynamics of the market participants, producing results that are entirely data-driven and free of bias. While traditional time series forecasting methods often struggle to account for nonlinear regime changes in growth, our approach successfully…

基于信息动力学的高阶交互网络表示AI 解读

Many complex systems in science and engineering are modeled as networks whose nodes and links depict the temporal evolution of each system unit and the dynamic interaction between pairs of units, which are assessed respectively using measures of auto- and cross-correlation or variants thereof. However, a growing body of work is documenting that this standard network representation can neglect potentially crucial information shared by three or more dynamic processes in the…

生态学中泰勒幂律参数的统计推断AI 解读

Taylor's power law (TL) or fluctuation scaling has been verified empirically for the abundances of many species, human and non-human, and in many other fields including physics, meteorology, computer science, and finance. TL asserts that the variance is directly proportional to a power of the mean, exactly for population moments and, whether or not population moments exist, approximately for sample moments. In many papers, linear regression of log variance as a function of…

利用可变带通周期块自助法分析纽约州新冠住院的周期性AI 解读

The outbreak of the SARS-CoV-2 virus, which led to an unprecedented global pandemic, has underscored the critical importance of understanding seasonal patterns. This knowledge is fundamental for decision-making in healthcare and public health domains. Investigating the presence, intensity, and precise nature of seasonal trends, as well as these temporal patterns, is essential for forecasting future occurrences, planning interventions, and making informed decisions based on…

利用总体时间序列一致估计扩散矩阵AI 解读

Progress on modern scientific questions regularly depends on using large-scale datasets to understand complex dynamical systems. An especially challenging case that has grown to prominence with advances in single-cell sequencing technologies is learning the behavior of individuals from population snapshots. In the absence of individual-level time series, standard stochastic differential equation models are often nonidentifiable because intrinsic diffusion cannot be…

计量电价预测模型：从日前预测到中长期预测AI 解读

The recent energy crisis starting in 2021 led to record-high gas, coal, carbon and power prices, with electricity reaching up to 40 times the pre-crisis average. This had dramatic consequences for operational and risk management prompting the need for robust econometric models for mid to long-term electricity price forecasting. After a comprehensive literature analysis, we identify key challenges and address them with novel approaches: 1) Fundamental information is…

利用局部统计矩估计 Kramers–Moyal 系数AI 解读

This study introduces an innovative local statistical moment approach for estimating Kramers-Moyal coefficients, effectively bridging the gap between nonparametric and parametric methodologies. These coefficients play a crucial role in characterizing stochastic processes. Our proposed approach provides a versatile framework for localized coefficient estimation, combining the flexibility of nonparametric methods with the interpretability of global parametric approaches. We…

多变量计数时间序列分析的潜在空间模型AI 解读

Motivated by a dataset of burglaries in Chicago, USA, we introduce a novel framework to analyze time series of count data combining common multivariate time series models with latent position network models. This novel methodology allows us to gain a new latent variable perspective on the crime dataset that we consider, allowing us to disentangle and explain the complex patterns exhibited by the data, while providing a natural time series framework that can be used to make…

非可交换数据下的分割保形预测AI 解读

Split conformal prediction (CP) is arguably the most popular CP method for uncertainty quantification, enjoying both academic interest and widespread deployment. However, the original theoretical analysis of split CP makes the crucial assumption of data exchangeability, which hinders many real-world applications. In this paper, we present a novel theoretical framework based on concentration inequalities and decoupling properties of the data, proving that split CP remains…