Review of ML and AutoML solutions to forecast time-series data
Time-series forecasting is a significant discipline of data modeling where past observations
of the same variable are analyzed to predict the future values of the time series. Its …
of the same variable are analyzed to predict the future values of the time series. Its …
Improved fault classification for predictive maintenance in industrial IoT based on AutoML: A case study of ball-bearing faults
The growing complexity of data derived from Industrial Internet of Things (IIoT) systems
presents substantial challenges for traditional machine-learning techniques, which struggle …
presents substantial challenges for traditional machine-learning techniques, which struggle …
Time series data modeling using advanced machine learning and AutoML
A Alsharef, K Kumar, C Iwendi - Sustainability, 2022 - mdpi.com
A prominent area of data analytics is “timeseries modeling” where it is possible to forecast
future values for the same variable using previous data. Numerous usage examples …
future values for the same variable using previous data. Numerous usage examples …
Towards green automated machine learning: Status quo and future directions
Automated machine learning (AutoML) strives for the automatic configuration of machine
learning algorithms and their composition into an overall (software) solution—a machine …
learning algorithms and their composition into an overall (software) solution—a machine …
A deep-learning-based health indicator constructor using kullback–leibler divergence for predicting the remaining useful life of concrete structures
This paper proposes a new technique for the construction of a concrete-beam health
indicator based on the Kullback–Leibler divergence (KLD) and deep learning. Health …
indicator based on the Kullback–Leibler divergence (KLD) and deep learning. Health …
Life prediction under charging process of lithium-ion batteries based on AutoML
Accurate online capacity estimation and life prediction of lithium-ion batteries (LIBs) are
crucial to large-scale commercial use for electric vehicles. The data-driven method lately has …
crucial to large-scale commercial use for electric vehicles. The data-driven method lately has …
Automated machine learning, bounded rationality, and rational metareasoning
The notion of bounded rationality originated from the insight that perfectly rational behavior
cannot be realized by agents with limited cognitive or computational resources. Research on …
cannot be realized by agents with limited cognitive or computational resources. Research on …
Conformal Prediction Intervals for Remaining Useful Lifetime Estimation
The main objective of Prognostics and Health Management is to estimate the Remaining
Useful Lifetime (RUL), namely, the time that a system or a piece of equipment is still in …
Useful Lifetime (RUL), namely, the time that a system or a piece of equipment is still in …
[PDF][PDF] Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting.
Y Su, MC Wang, S Liu - Computers, Materials & Continua, 2024 - cdn.techscience.cn
Long-term time series forecasting stands as a crucial research domain within the realm of
automated machine learning (AutoML). At present, forecasting, whether rooted in machine …
automated machine learning (AutoML). At present, forecasting, whether rooted in machine …
Meta-learning for automated selection of anomaly detectors for semi-supervised datasets
In anomaly detection, a prominent task is to induce a model to identify anomalies learned
solely based on normal data. Generally, one is interested in finding an anomaly detector that …
solely based on normal data. Generally, one is interested in finding an anomaly detector that …