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Accurate and Efficient Time Series Matching by Season- and Trend-aware Symbolic Approximation - Extended Version Including Additional Evaluation and Proofs., , , and . CoRR, (2021)CSAR: the cross-sectional autoregression model for short and long-range forecasting., , , , and . Int. J. Data Sci. Anal., 8 (2): 165-181 (2019)Simplicity Done Right for Join Ordering., , , and . CIDR, www.cidrdb.org, (2021)Cardinality estimation with local deep learning models., , , , and . aiDM@SIGMOD, page 5:1-5:8. ACM, (2019)Forecasting large-scale time series data.. Dresden University of Technology, Germany, (2018)PostBOUND: PostgreSQL with Upper Bound SPJ Query Optimization., , , , and . BTW, volume P-331 of LNI, page 283-304. Gesellschaft für Informatik e.V., (2023)Large-Scale Time Series Analytics - Novel Approaches for Generation and Prediction., , , and . Datenbank-Spektrum, 19 (1): 17-29 (2019)Best of both worlds: combining traditional and machine learning models for cardinality estimation., , , and . aiDM@SIGMOD, page 4:1-4:8. ACM, (2020)Season- and Trend-aware Symbolic Approximation for Accurate and Efficient Time Series Matching., , , and . Datenbank-Spektrum, 21 (3): 225-236 (2021)Turbo-Charging SPJ Query Plans with Learned Physical Join Operator Selections., , , and . Proc. VLDB Endow., 15 (11): 2706-2718 (2022)