ArticlePattern recognition letters2018
A novel imputation methodology for time series based on pattern sequence forecasting.
Article in Pattern recognition letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- An Interpretable Omics-to-Image Transformer Framework for Cancer Prognosis Prediction.Computational and structural biotechnology journal · 2026Article
- Quantitative evaluation of low-frequency oscillations using real-time phase-contrast MRI during drowsiness.Fluids and barriers of the CNS · 2025Article
- Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with ARIMA and LSTM.BMC medical research methodology · 2024Article
- Robust Multi-Dimensional Time Series Forecasting.Entropy (Basel, Switzerland) · 2024Article
- DFASGCNS: A prognostic model for ovarian cancer prediction based on dual fusion channels and stacked graph convolution.PloS one · 2024Article
- Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data.Methods of information in medicine · 2022Article
- Strategies for Imputation of High-Resolution Environmental Data in Clinical Randomized Controlled Trials.International journal of environmental research and public health · 2022Article
- Data Consistency for Data-Driven Smart Energy Assessment.Frontiers in big data · 2021Review
- An Adaptive Transfer-Learning-Based Deep Cox Neural Network for Hepatocellular Carcinoma Prognosis Prediction.Frontiers in oncology · 2021Article
- Deep learning-based ovarian cancer subtypes identification using multi-omics data.BioData mining · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The Pattern Sequence Forecasting (PSF) algorithm is a previously described algorithm that identifies patterns in time series data and forecasts values using periodic characteristics of the observations. A new method for univariate time series is introduced that modifies the PSF algorithm to simultaneously forecast and backcast missing values for imputation. The imputePSF method extends PSF by characterizing repeating patterns of existing observations to provide a more precise estimate of missing values compared to more conventional methods, such as replacement with means or last observation carried forward. The imputation accuracy of imputePSF was evaluated by simulating varying amounts of missing observations with three univariate datasets. Comparisons of imputePSF with well-established methods using the same simulations demonstrated an overall reduction in error estimates. The imputePSF algorithm can produce more precise imputations on appropriate datasets, particularly those with periodic and repeating patterns.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.