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 …

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

The imputation of missing values in time series has many applications in healthcare and
finance. While autoregressive models are natural candidates for time series imputation …

Recent work has shown that simple linear models can outperform several Transformer
based approaches in long term time-series forecasting. Motivated by this, we propose a …

We present Darts, a Python machine learning library for time series, with a focus on
forecasting. Darts offers a variety of models, from classics such as ARIMA to state-of-the-art …

Deep learning performs remarkably well on many time series analysis tasks recently. The
superior performance of deep neural networks relies heavily on a large number of training …

In this work, we propose TimeGrad, an autoregressive model for multivariate probabilistic
time series forecasting which samples from the data distribution at each time step by …

Real-world time-series datasets are often multivariate with complex dynamics. To capture
this complexity, high capacity architectures like recurrent-or attention-based sequential deep …

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 …

Forecasting high-dimensional time series plays a crucial role in many applications such as
demand forecasting and financial predictions. Modern datasets can have millions of …