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Vhf:A Time Series Hybrid Model Based on Variational Decomposition and Prior Knowledge Reconstruction for the Bio-Pharmaceutical Industry
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Big data and machine learning driven bioprocessing – Recent trends and critical analysis
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A decade in review: use of data analytics within the biopharmaceutical sector
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“Assumed inherent sensor” inversion based ANN dynamic soft-sensing method and its application in erythromycin fermentation process
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Hybrid deep learning and empirical mode decomposition model for time series applications
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Physicochemical characterization of biopharmaceuticals
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Fundamental limitations of network reconstruction from temporal data
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Physics-Based Convolutional Neural Network for Fault Diagnosis of Rolling Element Bearings
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Neural Decomposition of Time-Series Data for Effective Generalization
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Variational Mode Decomposition
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The Fitting of Non-Stationary Time-Series Models with Time-Dependent Parameters
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MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns
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Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
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Are Transformers Effective for Time Series Forecasting?
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Glove: Global Vectors for Word Representation
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Development and Optimization of a Novel Soft Sensor Modeling Method for Fermentation Process of Pichia pastoris
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Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
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FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
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Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures
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A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
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iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
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Wavelet Transform as a tool in the monitoring of fermentative processes through image processing
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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