keyword

temporal features

Temporal features are data attributes or variables that represent time-dependent properties and the sequential progression of observations within a dataset. In data analysis and machine learning, these features capture how values change over time, enabling algorithms to detect historical patterns, dependencies, and cyclical behaviors. They typically encompass direct calendar and clock attributes, such as hour of the day, day of the week, month, or season, as well as derived metrics, such as time-lagged observations, rolling window statistics, rates of change, and elapsed duration. By embedding temporal dynamics into models, temporal features allow systems to account for trends, seasonality, and sequential context when analyzing time-ordered data or forecasting future outcomes.

1 item

Time-series forecasting with deep learning: a survey

Bryan Lim, Stefan Zohren

OrganizationsUniversity of Oxford

Why you should read this

Categorizes modern deep learning and hybrid statistical architectures for multi-horizon time series forecasting, providing a clear guide on how different models encode temporal dynamics for operational decision support.

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 and decoder designs used in both one-step-ahead and multi-horizon time series forecasting -- describing how temporal information is incorporated into predictions by each model. Next, we highlight recent developments in hybrid deep learning models, which combine well-studied statistical models with neural network components to improve pure methods in either category. Lastly, we outline some ways in which deep learning can also facilitate decision support with time series data.

Source

https://arxiv.org/pdf/2004.13408

Added

2026-09-18