Time Series Analysis for Signal Processing
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 1
Special Issue Editors
Interests: time series; neural network; signal detection
Interests: signal processing; artificial intelligence; sound event detection
Special Issue Information
Dear Colleagues,
In an era defined by data-rich environments from the Internet of Things (IoT), biomedical sensors, and communication systems, the volume and complexity of temporal signals are growing exponentially. This deluge presents a critical challenge: extracting meaningful information and actionable insights from noisy, high-dimensional, and non-stationary time series data. Traditional signal processing techniques, while foundational, often struggle with the scale and intricate patterns inherent in complex datasets. Concurrently, the field of deep time-series modeling has revolutionized forecasting and pattern recognition. Yet, these models often operate as "black boxes," lacking the interpretability and robustness required for sensitive signal-processing applications. This Special Issue positions entropy, mutual information, and related measures as principled tools for representation learning, model selection, uncertainty quantification, and rigorous evaluation in complex signal domains.
We invite research that unifies deep learning and signal processing via information-theoretic principles to improve transparency, efficiency, and reliability in intelligent signal analysis. Submissions may, for example, ground explainable AI in entropy/MI-based objectives, integrate physical knowledge with data-driven models under information-consistent constraints, or advance self-supervised learning tailored to unlabeled, multimodal, and non-stationary signals; equally welcome are new architectures and training schemes for denoising, anomaly detection, sound event detection, and classification that demonstrate robustness, sample-efficiency, and calibrated uncertainty under realistic conditions. Our goal is to curate contributions that achieve state-of-the-art performance while making the information flow within models explicit, enabling reproducible benchmarking, and establishing theory-guided principles for trustworthy signal processing at scale.
Dr. Ruobin Gao
Dr. Yang Yu
Dr. Zicheng Wang
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- time series analysis
- time series forecasting
- long-range forecasting
- neural networks
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