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Open research questions in Time Series Analysis and Forecasting

53 unresolved questions extracted from the limitations and future-work sections of 350 Time Series Analysis and Forecasting papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Quadratic complexity of the self-attention mechanism. Permutation-invariant bias of the self-attention mechanism. Limited flexibility of the scan order in MambaTS.

    MambaTS: Improved selective state space models for long-term time series forecasting · 2026 · DOI

  • The lack of independence and identically distributed errors in the time series. The need to construct asymptotically valid permutation tests. The need to retain the finite sample exactness property under the additional assumption that the randomization hypothesis holds.

    Least squares-based permutation tests in time series · 2026 · DOI

  • The lack of Type 1 and Type 3 error control in permutation tests when the randomization hypothesis does not hold. The need for a method to test for no monotone trend in a time series.

    Least squares-based permutation tests in time series · 2026 · DOI

  • Global dependencies are stable but adapt poorly to sample variations and temporal non-stationarity, whereas local dependencies are adaptive yet unreliable when observations are insufficient, causing erroneous information propagation.

    GLAIM: Learning Global and Local Adaptive Inter-Variable Dependency for Multivariate Time Series Imputation · 2026

  • Correctly identifying the model in interrupted time series analysis. Dealing with limited data in applied research settings. Determining the optimal number of observations required for accurate model identification.

    The Reliability and Accuracy of Time Series Model Identification · 1983 · DOI

  • The study uses a limited number of observations (N = 40 and N = 100). The study uses simulated data generated by a computer program.

    The Reliability and Accuracy of Time Series Model Identification · 1983 · DOI

  • Evaluating the proposed approach using multiple datasets. Comparing the proposed approach with other time series storage and analysis methods. Applying the proposed approach to other industrial and IoT systems.

    Problems and Approaches to Storing and Analyzing Time Series under Conditions of Feature Redundancy · 2026 · DOI

  • Traditional relational database management systems are poorly suited for time series data. Feature redundancy in time series data can reduce the effectiveness of machine learning models and increase storage costs.

    Problems and Approaches to Storing and Analyzing Time Series under Conditions of Feature Redundancy · 2026 · DOI

  • To explore and incorporate other advanced clustering ensemble algorithms. To apply RACER to other domains and tasks.

    RACER: Fast and Accurate Time Series Clustering With Random Convolutional Kernels and Ensemble Methods · 2026 · DOI

  • Despite advances in univariate time-series (UTS) clustering, multivariate (MTS) clustering remains underexplored—multiple channels complicate inter-channel dependency modeling and amplify the accuracy–runtime trade-off.

    MUFASA: Fast and Accurate Multivariate Time-Series Clustering · 2026 · DOI

  • Traditional machine learning models are not able to utilize the sequential ordering of data points and temporal dependencies. Manual feature engineering presents many trade-offs such as extensive development hours, subpar performance, and inflexible feature quality.

    Autonomous Feature Engineering Agent for Time-Series Tabular Data · 2026 · DOI

  • Future research can apply the framework to other application domains, such as finance and social networks. Future research can explore the use of other machine learning techniques for the classification task. Future research can investigate the use of the framework for real-time analysis of event sequences.

    Analysis framework for higher-order temporal correlations with applications to human heartbeats · 2026 · DOI

  • The paper identifies a gap in the analysis of high-order temporal correlations in event sequences. The paper identifies a need for a novel analysis framework that can reveal the hierarchical structure of bursts.

    Analysis framework for higher-order temporal correlations with applications to human heartbeats · 2026 · DOI

  • To develop a Modified Prophet Method (MPM) that enhances automated component identification and improves volatility handling. To examine the performance of the Modified Prophet Model in different data contexts. To explore the application of the Modified Prophet Model in various domains.

    A COMPREHENSIVE STUDY OF MODIFIED PROPHET MODEL FOR TIME SERIES COMPONENT IDENTIFICATION · 2026 · DOI

  • The Prophet model has limitations in automated component identification. The model has limited statistical decomposition transparency. The model assumes relatively smooth structural changes and may underperform in highly volatile empirical data.

    A COMPREHENSIVE STUDY OF MODIFIED PROPHET MODEL FOR TIME SERIES COMPONENT IDENTIFICATION · 2026 · DOI

  • Traditional Shapelet-based methods often necessitate considerable computational resources. Deep learning methods lack interpretability, making it difficult to understand the decision-making process.

    E-STAR: enhanced Shapelet-Integrated frequency and temporal model representation for advanced time series classification · 2026 · DOI

  • To apply the method to other types of time series data. To explore the use of other correction models. To investigate the application of the method to other fields.

    Surrogate Modeling for Explainable Predictive Time Series Corrections · 2026 · DOI

  • There is a need for methods that can provide explanations for time series data. Current methods have limitations, such as uncertainties in the models.

    Surrogate Modeling for Explainable Predictive Time Series Corrections · 2026 · DOI

  • The scarcity of large-scale, real-world time series with precisely annotated kinematic changes is a primary impediment to applying supervised deep learning. The lack of effective methods for detecting kinematic change points in InSAR time series with significant temporal gaps is a significant research gap.

    A Robust Attention-Based Time-Gated LSTM for Change Point Detection in Challenging InSAR Time Series with Data Gaps · 2026 · DOI

  • The permutation tests have limited power in short timeseries with substantial measurement error. The tests are computationally expensive for large datasets. The application of the tests to empirical datasets is limited by the availability of high-quality data.

    Model-free inference of evolution from allele frequency timeseries using permutation tests · 2026 · DOI

  • The development of more powerful permutation tests for short timeseries data. The application of the tests to larger and more diverse datasets. The integration of the tests with other analytical tools to improve the interpretation of allele frequency timeseries data.

    Model-free inference of evolution from allele frequency timeseries using permutation tests · 2026 · DOI

  • Further development of pyhctsa to include additional time-series analysis methods. Application of pyhctsa to various domains, such as medical diagnosis and demand forecasting.

    pyhctsa: A Python package for highly comparative time-series analysis · 2026 · DOI

  • The lack of a comprehensive time-series feature set in native Python. The proprietary implementation of hctsa is a barrier to open science and industry applications.

    pyhctsa: A Python package for highly comparative time-series analysis · 2026 · DOI

  • Existing methods do not account for local interactions in high-dimensional matrix and tensor time series. The need for a unified framework that accommodates high dimensionality and multi-way structure.

    Local Interaction Autoregressive Model for High Dimension Time Series Data · 2026 · DOI

  • Future research can explore the application of the representation to other time series data. Future research can explore the use of different density estimation methods.

    Time-Series Motion States: Relative Position, Velocity, and Historical Rarity · 2026 · DOI

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53 open questions have been extracted from the limitations and future-work passages of 350 Time Series Analysis and Forecasting papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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