The Journal of Time Series Analysis publishes research on statistical inference for time series models. This includes topics such as estimation, hypothesis testing, and forecasting for various time series structures, including those with time-varying coefficients, conditional heteroskedasticity, and non-Gaussian noise. The journal also covers methods for detecting structural breaks, change points, and outliers in time series data, as well as applications in econometrics and finance.
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Publication & Citation Trend
Articles published
Times cited
2019
2020
2021
2022
2023
2024
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2026
Source: OpenAlex · Note: citations accumulate over time so older years appear higher
SJR Quartile by Discipline
Scimago ranks this journal separately in each subject category — its quartile can differ by discipline.
Applied MathematicsQ1
Statistics and ProbabilityQ1
Statistics, Probability and UncertaintyQ1
Subject Classification
Web of Science Categories
Mathematics, Interdisciplinary ApplicationsStatistics & Probability
Scopus Categories
Applied MathematicsStatistics, Probability and UncertaintyStatistics and Probability
Research Topics (OpenAlex)
Financial Risk and Volatility ModelingStatistical Methods and InferenceMonetary Policy and Economic ImpactComplex Systems and Time Series AnalysisAdvanced Statistical Methods and Models