Abstract Recurrent Neural Networks (RNNs) have become competitive forecasting methods,
as most notably shown in the winning method of the recent M4 competition. However …

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 …

Recent progress in neural forecasting accelerated improvements in the performance of large-
scale forecasting systems. Yet, long-horizon forecasting remains a very difficult task. Two …

Probabilistic forecasting, ie, estimating a time series' future probability distribution given its
past, is a key enabler for optimizing business processes. In retail businesses, for example …

We introduce Chronos, a simple yet effective framework for pretrained probabilistic time
series models. Chronos tokenizes time series values using scaling and quantization into a …

Current approaches for multi-horizon time series forecasting using recurrent neural networks
(RNNs) focus on issuing point estimates, which is insufficient for decision-making in critical …

Generative modeling of multivariate time series has remained challenging partly due to the
complex, non-deterministic dynamics across long-distance timesteps. In this paper, we …

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

We present a probabilistic forecasting framework based on convolutional neural network
(CNN) for multiple related time series forecasting. The framework can be applied to estimate …

Predicting the dependencies between observations from multiple time series is critical for
applications such as anomaly detection, financial risk management, causal analysis, or …