Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Deep learning based forecasting methods have become the methods of choice in many
applications of time series prediction or forecasting often outperforming other approaches …

Multivariate time series data comprises various channels of variables. The multivariate
forecasting models need to capture the relationship between the channels to accurately …

Global methods that fit a single forecasting method to all time series in a set have recently
shown surprising accuracy, even when forecasting large groups of heterogeneous time …

Large Language Models (LLMs) have seen significant use in domains such as natural
language processing and computer vision. Going beyond text, image and graphics, LLMs …

We present coarse-to-fine autoregressive networks (C2FAR), a method for modeling the
probability distribution of univariate, numeric random variables. C2FAR generates a …

We propose SutraNets, a novel method for neural probabilistic forecasting of long-sequence
time series. SutraNets use an autoregressive generative model to factorize the likelihood of …

Time Series Classification (TSC) is a crucial area in machine learning. Although applications
of Deep Neural Networks (DNNs) in this area have led to relatively good results, classifying …

Many time series classification algorithms have been proposed, including deep neural
networks based, which so far focused mainly on improving model architectures rather than …

Time series models aim for accurate predictions of the future given the past, where the
forecasts are used for important downstream tasks like business decision making. In …