Time series forecasting is fundamental for various use cases in different domains such as
energy systems and economics. Creating a forecasting model for a specific use case …

Abstract Machine learning (ML) is becoming increasingly crucial in many fields of
engineering but has not yet played out its full potential in bioprocess engineering. While …

Many businesses and industries nowadays rely on large quantities of time series data
making time series forecasting an important research area. Global forecasting models that …

Synthetic Time Series Generation (TSG) is crucial in a range of applications, including data
augmentation, anomaly detection, and privacy preservation. Although significant strides …

Modeling multivariate time series has been a subject for a long time, which attracts the
attention of scholars from many fields including economics, finance, traffic, etc. As the …

Time Series Forecasting (TSF) is key functionality in numerous fields, including in finance,
weather services, and energy management. While TSF methods are emerging these days …

Time series are generated in diverse domains such as economic, traffic, health, and energy,
where forecasting of future values has numerous important applications. Not surprisingly …

Time series are critical for decision-making in fields like finance and healthcare. Their
importance has driven a recent influx of works passing time series into language models …

Dissolved oxygen concentration is crucial for aquaculture systems. Accurately predicting
dissolved oxygen levels in advance provides valuable information to implement necessary …

Optimizing the time-series forecasting performance is a multi-objective problem which
enables the comparison of general applicability of methods across multiple use cases such …