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Found 14 papers, 0 papers with code
Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning
Platform businesses operate on a digital core and their decision making requires high-dimensional accurate forecast streams at different levels of cross-sectional (e. g., geographical regions) and temporal aggregation (e. g., minutes to days).
Monitoring Machine Learning Forecasts for Platform Data Streams
To ensure accurate and stable forecasts, we propose a simple data-driven monitoring procedure to answer the question when the ML algorithm should be re-trained.
Tree-based Forecasting of Day-ahead Solar Power Generation from Granular Meteorological Features
Accurate forecasts for day-ahead photovoltaic (PV) power generation are crucial to support a high PV penetration rate in the local electricity grid and to assure stability in the grid.
Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms
On-demand service platforms face a challenging problem of forecasting a large collection of high-frequency regional demand data streams that exhibit instabilities.
Sparse High-Dimensional Vector Autoregressive Bootstrap
We introduce a high-dimensional multiplier bootstrap for time series data based capturing dependence through a sparsely estimated vector autoregressive model.
Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions
Reverse Unrestricted MIxed DAta Sampling (RU-MIDAS) regressions are used to model high-frequency responses by means of low-frequency variables.
Local Projection Inference in High Dimensions
In this paper, we estimate impulse responses by local projections in high-dimensional settings.
Detecting Anti-dumping Circumvention: A Network Approach
Despite the increasing integration of the global economic system, anti-dumping measures are a common tool used by governments to protect their national economy.
Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions
Mixed-frequency Vector AutoRegressions (MF-VAR) model the dynamics between variables recorded at different frequencies.
Tree-based Node Aggregation in Sparse Graphical Models
High-dimensional graphical models are often estimated using regularization that is aimed at reducing the number of edges in a network.
bootUR: An R Package for Bootstrap Unit Root Tests
Unit root tests form an essential part of any time series analysis.
Lasso Inference for High-Dimensional Time Series
In this paper we develop valid inference for high-dimensional time series.
Interpretable Vector AutoRegressions with Exogenous Time Series
The Vector AutoRegressive (VAR) model is fundamental to the study of multivariate time series.
High Dimensional Forecasting via Interpretable Vector Autoregression
Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series.
