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Your complete how-to guide to putting ML to work - plus code samples and notebooks...

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This paper develops the theory of multi-step ahead forecasting for vector time series that exhibit temporal nonstationarity and co-integration. We treat the case of a semi-infinite past by developing ...

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Dynamic Baysian Models for Vector Time Series Analysis and Forecasting

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Strategies to Effectively Interpret and Evaluate Data Visualizations...

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Three Essays on Updating Forecasts in Vector Autoregression Models

By Hui Zhu

Forecasting firms' earnings has long been an interest of market participants and academics. Traditional forecasting studies in a multivariate time series setting do not take into account that the timi...

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By Svein Linge, Hans Petter Langtangen

A Gentle Introduction to Numerical Simulations with Python 3.6...

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The #1 Platform for Connected Data...

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9th International Conference, IHCI 2017, Evry, France, December 11-13, 2017, Proceedings...

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Forecasting with generalized Bayesian vector autoregressions

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No description available....

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Forecasting with Bayesian Vector Autoregressions with Time Variation in the Mean

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We develop a vector autoregressive model with time variation in the mean and the variance. The unobserved time-varying mean is assumed to follow a random walk and we also link it to long-term Consensu...

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Tools and algorithms for analyzing images...

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Forecast Skill of Targeted Observations: a Singular Vector Based Diagnostic

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Forecasting in Vector Autoregressions with Many Predictors

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This paper addresses the issue of improving the forecasting performance of vector autoregressions (VARs) when the set of available predictors is inconveniently large to handle with methods and diagnos...

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