Forecasting occupancy rate with Bayesian compression methods

TL;DR: In this article, the authors of the paper this article discuss the role of robots in the development of literature in the context of robot-assisted learning and propose a plana cikmaktadir.

TL;DR: In this paper, the authors employed online customer rating scores and review text provided by booking systems to forecast monthly hotel occupancy using long short-term memory networks (LSTMs) using online customer reviews of hotels in Taiwan in various languages.

TL;DR: In this article, the authors used crisis severity, approximated by social media data, combined with traditional time-series models, including SARIMA, ETS and STL models, to improve the accuracy of hotel demand forecast during periods of crisis or volatility.

TL;DR: This study proposes a deep learning approach, LICNN, to leverage online reviews for hotel demand forecasting, achieving improved accuracy (2.2-46.6% RMSE reduction) and outperforming baseline models by 8.1-56.1% in error metrics.

TL;DR: The Lasso estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the regression parameters have independent Laplace (i.e., double-exponential) priors.

TL;DR: In this paper, monetary policy and the private sector behavior of the US economy are modeled as a time varying structural vector autoregression, where the sources of time variation are both the coefficients and the variance covariance matrix of the innovations.

TL;DR: In this article, a generalized dynamic factor model with infinite dynamics and nonorthogonal idiosyncratic components is proposed, which generalizes the static approximate factor model of Chamberlain and Rothschild (1983), as well as the exact factor model a la Sargent and Sims (1977).

TL;DR: The paper revisits the inflation forecasting problem posed by Stock and Watson (1999), and compute the model confidence set (MCS) for their set of inflation forecasts, and compares a number of Taylor rule regressions to determine the MCS of the best in terms of in-sample likelihood criteria.