By-Product Data and Forecasting in Unemployment Insurance
失业保险运行附带数据及其预测用途AI 解读 ↓
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失业保险运行附带数据及其预测用途AI 解读 ↓
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The unemployment insurance program has been operating in this country since the beginning of 1938. There have been many growing pains during these first eleven years. The operating problems involved in setting up this vast program have left little time thus far for careful consideration of the statistical data that are automatically produced in the course of its operations. These data, now and in the future, can be a fruitful source of information on changes in economic activities. The comprehensive nature of these data, covering every city and county, giving information by detailed sub-industry, and yielding statistics for each week and month, makes it possible to test various theories of the business cycle, and may, as a result of careful analysis, reveal interrelationships among economic variables that are now unsuspected. The data described in this paper are collected by all the state unemployment insurance agencies as part of their operations. However, some states publish only the statistics which are needed for administrative and operating purposes. Undoubtedly, more would be published if the existence of these valuable materials became generally known.
循环重心编码器:预测、模拟及其在时间序列分析中的应用
This dissertation has two parts. In the first part we theoretically develop a framework that extends the optimal transport barycenter problem from independent data with observed covariates to processes whose relevant covariate is their own history. Given a stationary stochastic process, our framework shows there exists an invertible transport map that extracts from it an iid component whose distribution is the barycenter of the conditional distributions of the input process…
协整回归变量:折叠凹惩罚何时超越 LASSO
LASSO is one of the most popular regularization methods for high-dimensional regression models. While cointegration is common in practical applications of predictive regressions with many regressors, the convex penalty of LASSO overly suppresses the coefficients of cointegrated regressors and therefore fails to deliver consistent parameter estimation. We employ folded concave penalties (FCP), including the well-known SCAD and MCP, to address this issue. Using FCP, we…
基于深度可分离神经网络的混频时间序列预测
To better forecast mixed-frequency time series, it is the key to choose a suitable way for frequency alignment. However, the existing methods are all limited to linear transformations, and this may overlook the possible nonlinearity, leading to a worse prediction. We alternatively consider a deep neural network for each frequency alignment, and hence a depth-separable neural network. Moreover, a parameter-sharing mechanism is adopted across the alignment at each stage,…
跨空间时间序列的预测驱动推断
The following motif is common in spatiotemporal settings: we have a sequence of covariate and label pairs observed for a relatively short, recent time period. We have access to unlabeled covariates over a longer time period. Data is observed over many spatial locations. For instance, crop yield might be observed over a large geographical area for recent years, but weather data (which is informative about crop yield) is available for a much longer period. The goal is to…
藤Copula VAR:从递归边际估计到联合预测推断
Joint-event forecasts often combine a dependence estimate based on past forecast errors with newly estimated marginal distributions. When each historical error retains the marginal fit available at its issue date, inference must account for an overlapping sequence of estimation errors. We derive their joint influence with the terminal forecast estimates in a stable Vine Copula VAR with normal innovation margins and a fixed, correctly specified Gaussian or positive Clayton…
部分观测时间序列的潜变量驱动插补与预测:LIFTS
Partially observed multivariate time series pose two coupled challenges: recovering missing measurements and forecasting future trajectories when observation patterns are informative. Existing methods often assume ignorable missingness or use the mask only as an auxiliary input. We propose LIFTS (Latent-driven Imputation and Forecasting for partly observed Time Series), a probabilistic framework in which a latent process jointly governs the complete measurement trajectory…