TOPIC

time series

Aggregated news, research, and updates specifically regarding time series. Auto-curated by our AI Engine.

research #data preprocessing 📝 BlogAnalyzed: Jan 13, 2026 17:00

Rolling Aggregation: A Practical Guide to Data Preprocessing with AI

Published:Jan 13, 2026 16:45

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1 min read

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Qiita AI

Analysis

This article outlines the creation of rolling aggregation features, a fundamental technique in time series analysis and data preprocessing. However, without more detail on the Python implementation, the specific data used, or the application of Gemini, its practical value is limited to a very introductory overview.

Key Takeaways

•Focuses on rolling aggregation features for data preprocessing.
•Uses Python for implementation.
•Leverages AI, specifically Gemini, for application.

Reference

“AIでデータ分析-データ前処理(51)-集計特徴量：ローリング集計特徴量の作...”

Permalink Qiita AI

research #feature engineering 📝 BlogAnalyzed: Jan 12, 2026 16:45

Lag Feature Engineering: A Practical Guide for Data Preprocessing in AI

Published:Jan 12, 2026 16:44

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1 min read

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Qiita AI

Analysis

This article provides a concise overview of lag feature creation, a crucial step in time series data preprocessing for AI. While the description is brief, mentioning the use of Gemini suggests an accessible, hands-on approach leveraging AI for code generation or understanding, which can be beneficial for those learning feature engineering techniques.

Key Takeaways

•The article focuses on creating lag features, which is essential for time series data analysis.
•It presents a practical application using Python for implementation.
•The use of Gemini AI for assistance indicates a potential for code generation or understanding.

Reference

“The article mentions using Gemini for implementation.”

Permalink Qiita AI

Machine Learning #Time Series Analysis, Knowledge Distillation, Efficiency 📝 BlogAnalyzed: Jan 16, 2026 01:52

MemKD: Memory-Discrepancy Knowledge Distillation for Efficient Time Series Classification

Published:Jan 16, 2026 01:52

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1 min read

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Analysis

The article introduces a new method called MemKD for efficient time series classification. This suggests potential improvements in speed or resource usage compared to existing methods. The focus is on Knowledge Distillation, which implies transferring knowledge from a larger or more complex model to a smaller one. The specific area is time series data, indicating a specialization in this type of data analysis.

Key Takeaways

•MemKD is a new method for time series classification.
•It utilizes Knowledge Distillation to potentially improve efficiency.
•Focuses on optimizing performance for time series data.

Reference

“”

Permalink

research #rnn 📝 BlogAnalyzed: Jan 6, 2026 07:16

Demystifying RNNs: A Deep Learning Re-Learning Journey

Published:Jan 6, 2026 01:43

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1 min read

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Qiita DL

Analysis

The article likely addresses a common pain point for those learning deep learning: the relative difficulty in grasping RNNs compared to CNNs. It probably offers a simplified explanation or alternative perspective to aid understanding. The value lies in its potential to unlock time-series analysis for a wider audience.

Key Takeaways

•RNNs are often perceived as more difficult to understand than CNNs.
•The article aims to simplify the understanding of RNNs.
•The author likely shares their personal experience of re-learning deep learning.

Reference

“"CNN（畳み込みニューラルネットワーク）は理解できたが、RNN（リカレントニューラルネットワーク）がスッと理解できない"”

Permalink Qiita DL

research #timeseries 🔬 ResearchAnalyzed: Jan 5, 2026 09:55

Deep Learning Accelerates Spectral Density Estimation for Functional Time Series

Published:Jan 5, 2026 05:00

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1 min read

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ArXiv Stats ML

Analysis

This paper presents a novel deep learning approach to address the computational bottleneck in spectral density estimation for functional time series, particularly those defined on large domains. By circumventing the need to compute large autocovariance kernels, the proposed method offers a significant speedup and enables analysis of datasets previously intractable. The application to fMRI images demonstrates the practical relevance and potential impact of this technique.

Key Takeaways

•Proposes a deep learning estimator for spectral density of functional time series.
•Avoids computation of large autocovariance kernels, enabling faster computation.
•Validated with simulations and application to fMRI images.

Reference

“Our estimator can be trained without computing the autocovariance kernels and it can be parallelized to provide the estimates much faster than existing approaches.”

Permalink ArXiv Stats ML

Research #Time Series 🔬 ResearchAnalyzed: Jan 10, 2026 07:09

Change-Point Detection in Ornstein-Uhlenbeck Processes: A Sequential Approach

Published:Dec 26, 2025 23:54

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1 min read

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ArXiv

Analysis

This ArXiv paper likely presents novel methods for detecting changes in the statistical properties of Ornstein-Uhlenbeck processes, a common stochastic model. The research could have significant implications for various applications involving time series analysis and signal processing.

Key Takeaways

•Focuses on sequential change-point detection.
•Applies to generalized Ornstein-Uhlenbeck processes.
•Aims to improve change detection accuracy in stochastic models.

Reference

“The paper focuses on change-point detection for generalized Ornstein-Uhlenbeck processes.”

time series

Rolling Aggregation: A Practical Guide to Data Preprocessing with AI

Analysis

Key Takeaways

Lag Feature Engineering: A Practical Guide for Data Preprocessing in AI

Analysis

Key Takeaways

MemKD: Memory-Discrepancy Knowledge Distillation for Efficient Time Series Classification

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Demystifying RNNs: A Deep Learning Re-Learning Journey

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Deep Learning Accelerates Spectral Density Estimation for Functional Time Series

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Change-Point Detection in Ornstein-Uhlenbeck Processes: A Sequential Approach

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Improving Zero-Shot Time Series Forecasting with Noise Injection in LLMs

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Novel Forecasting Method Improves Control and Probabilistic Accuracy

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Point-wise Loss Bias in Time Series Optimization

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Aligning Incomplete Time Series Data: A New Approach

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KOSS: Improving Long-Term Sequence Modeling with Kalman Filtering

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Explainable and Effective Forecasting with Conversational Time Series Foundation Models

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Novel Time Series Analysis Technique for Biological Data Unveiled

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Gaussian Processes for Time-Series Analysis of Vector Sets

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FADTI: Advanced Time Series Imputation with Fourier and Attention

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Human-Centered Counterfactual Explanations for Time Series Interventions

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FLAME: Advancing Time Series Forecasting with Flow-Enhanced Legendre Memory Models

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New Time Series Analysis Method Uses Time-Frequency Fusion and Adaptive Denoising

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Advanced Time Series Forecasting: A Hybrid Graph Neural Network Approach

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DARTs: A Novel Framework for Anomaly Detection in Time Series Data

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Explainable AI for Economic Time Series: Review and Taxonomy

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SigTime: Visualizing and Explaining Time Series Signatures Through Deep Learning

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Data Mixture Search: Enhancing Time Series Forecasting Training

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Contrastive Learning for Time Series Forecasting: Addressing Anomalies

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Insight Miner: New Dataset for Time Series Analysis and Natural Language Alignment

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Adaptive Information Routing Improves Time Series Forecasting

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Recovering Missing Time Series Data with Isometric Delay-Embedding