Search Results for author:
Found 11 papers, 8 papers with code
Chronos: Learning the Language of Time Series
We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models.
Deep Non-Parametric Time Series Forecaster
This paper presents non-parametric baseline models for time series forecasting.
Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient
Backtracking linesearch is the de facto approach for minimizing continuously differentiable functions with locally Lipschitz gradient.
Normalizing Kalman Filters for Multivariate Time Series Analysis
This paper tackles the modelling of large, complex and multivariate time series panels in a probabilistic setting.
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models
Our method is amenable to streaming anomaly detection and scales to monitoring for anomalies on millions of time series.
Douglas-Rachford splitting and ADMM for nonconvex optimization: Accelerated and Newton-type linesearch algorithms
Although the performance of popular optimization algorithms such as Douglas-Rachford splitting (DRS) and the ADMM is satisfactory in small and well-scaled problems, ill conditioning and problem size pose a severe obstacle to their reliable employment.
Optimization and Control 90C06, 90C25, 90C26, 49J52, 49J53
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches.
GluonTS: Probabilistic Time Series Models in Python
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
Deep State Space Models for Time Series Forecasting
We present a novel approach to probabilistic time series forecasting that combines state space models with deep learning.
Forward-backward envelope for the sum of two nonconvex functions: Further properties and nonmonotone line-search algorithms
Extending previous results we show that, despite being nonsmooth for fully nonconvex problems, the FBE still enjoys favorable first- and second-order properties which are key for the convergence results of ZeroFPR.
Optimization and Control 90C06, 90C25, 90C26, 90C53, 49J52, 49J53
Forward-backward quasi-Newton methods for nonsmooth optimization problems
We propose an algorithmic scheme that enjoys the same global convergence properties of FBS when the problem is convex, or when the objective function possesses the Kurdyka-{\L}ojasiewicz property at its critical points.
Optimization and Control
