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 …

Forecast combinations have flourished remarkably in the forecasting community and, in
recent years, have become part of mainstream forecasting research and activities …

We propose an automated method for obtaining weighted forecast combinations using time
series features. The proposed approach involves two phases. First, we use a collection of …

Anomaly detection is a significant problem that has been studied in a broader spectrum of
research areas due to its diverse applications in different domains. Despite the usage of …

Retail sales forecasting often requires forecasts for thousands of products for many stores.
We present a meta-learning framework based on newly developed deep convolutional …

The explosion of time series data in recent years has brought a flourish of new time series
analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of …

Time series data appears in many real-world fields such as energy, transportation,
communication systems. Accurate modelling and forecasting of time series data can be of …

The M4 competition identified innovative forecasting methods, advancing the theory and
practice of forecasting. One of the most promising innovations of M4 was the utilization of …

The fluctuations of agricultural commodity prices have a great impact on people's daily lives
as well as the inputs and outputs of agricultural production. An accurate forecast of …

Feature-based time series representations have attracted substantial attention in a wide
range of time series analysis methods. Recently, the use of time series features for forecast …