The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …

Numerous deep learning architectures have been developed to accommodate the diversity
of time-series datasets across different domains. In this article, we survey common encoder …

Transformers have shown great power in time series forecasting due to their global-range
modeling ability. However, their performance can degenerate terribly on non-stationary real …

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 …

Extending the forecasting time is a critical demand for real applications, such as extreme
weather early warning and long-term energy consumption planning. This paper studies the …

This paper presents TS2Vec, a universal framework for learning representations of time
series in an arbitrary semantic level. Unlike existing methods, TS2Vec performs contrastive …

Many real-world applications require the prediction of long sequence time-series, such as
electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a …

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 …

Multi-horizon forecasting often contains a complex mix of inputs–including static (ie time-
invariant) covariates, known future inputs, and other exogenous time series that are only …

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 …