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time (248)
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Stem seri$ (all stems)

310 papers:

SIGMODSIGMOD-2015-AllardHMP #clustering #named #privacy
Chiaroscuro: Transparency and Privacy for Massive Personal Time-Series Clustering (TA, GH, FM, EP), pp. 779–794.
SIGMODSIGMOD-2015-PaparrizosG #clustering #named #performance
k-Shape: Efficient and Accurate Clustering of Time Series (JP, LG), pp. 1855–1870.
SIGMODSIGMOD-2015-SakuraiMF #mining
Mining and Forecasting of Big Time-series Data (YS, YM, CF), pp. 919–922.
SIGMODSIGMOD-2015-ZhouT #named #predict
SMiLer: A Semi-Lazy Time Series Prediction System for Sensors (JZ, AKHT), pp. 1871–1886.
VLDBVLDB-2015-DingWDFZZ #clustering #named #performance #scalability
YADING: Fast Clustering of Large-Scale Time Series Data (RD, QW, YD, QF, HZ, DZ), pp. 473–484.
VLDBVLDB-2015-PelkonenFCHMTV #database #in memory #named #performance #scalability
Gorilla: A Fast, Scalable, In-Memory Time Series Database (TP, SF, PC, QH, JM, JT, KV), pp. 1816–1827.
VLDBVLDB-2015-ZoumpatianosIP #interactive #named
RINSE: Interactive Data Series Exploration with ADS+ (KZ, SI, TP), pp. 1912–1923.
SANERSANER-2015-BaoLXWZ #interactive #reverse engineering
Reverse engineering time-series interaction data from screen-captured videos (LB, JL, ZX, XW, BZ), pp. 399–408.
LATALATA-2015-BaillyDR
Recognizable Series on Hypergraphs (RB, FD, GR), pp. 639–651.
ICEISICEIS-v1-2015-Xylogiannopoulos #detection #roadmap
Discretization Method for the Detection of Local Extrema and Trends in Non-discrete Time Series (KFX, PK, RA), pp. 346–352.
ICEISICEIS-v2-2015-SouzaVS #analysis #semantics
Semantically Enriching the Detrending Step of Time Series Analysis (LdS, MSMGV, MSS), pp. 475–481.
ICMLICML-2015-AnavaHZ #online #predict
Online Time Series Prediction with Missing Data (OA, EH, AZ), pp. 2191–2199.
ICMLICML-2015-BahadoriKFL #clustering #functional
Functional Subspace Clustering with Application to Time Series (MTB, DCK, YF, YL), pp. 228–237.
KDDKDD-2015-BarajasA #approach #health #modelling
Dynamically Modeling Patient’s Health State from Electronic Medical Records: A Time Series Approach (KLCB, RA), pp. 69–78.
KDDKDD-2015-CaiTFJH #higher-order #mining #named #performance
Facets: Fast Comprehensive Mining of Coevolving High-order Time Series (YC, HT, WF, PJ, QH), pp. 79–88.
KDDKDD-2015-LaptevAF #automation #detection #framework #scalability
Generic and Scalable Framework for Automated Time-series Anomaly Detection (NL, SA, IF), pp. 1939–1947.
KDDKDD-2015-Shokoohi-Yekta0
Discovery of Meaningful Rules in Time Series (MSY, YC, BJLC, BH, JZ, EJK), pp. 1085–1094.
KDDKDD-2015-UlanovaYCJKZ #performance #physics #profiling
Efficient Long-Term Degradation Profiling in Time Series for Complex Physical Systems (LU, TY, HC, GJ, EJK, KZ), pp. 2167–2176.
KDDKDD-2015-ZoumpatianosLPG #query
Query Workloads for Data Series Indexes (KZ, YL, TP, JG), pp. 1603–1612.
MLDMMLDM-2015-AkbariniaM #probability #streaming
Aggregation-Aware Compression of Probabilistic Streaming Time Series (RA, FM), pp. 232–247.
SEKESEKE-2015-TunnellA #fault #modelling #predict #release planning #using
Using Time Series Models for Defect Prediction in Software Release Planning (JT, JA), pp. 451–454.
CASECASE-2014-YamazakiSYI #3d #modelling
3D shape modeling of movable parts of furniture based on time-series surface correspondence (KY, KS, TY, MI), pp. 249–254.
SIGMODSIGMOD-2014-ZoumpatianosIP #big data #interactive
Indexing for interactive exploration of big data series (KZ, SI, TP), pp. 1555–1566.
VLDBVLDB-2014-JugelJM #named #visualisation
M4: A Visualization-Oriented Time Series Data Aggregation (UJ, ZJ, GH, VM), pp. 797–808.
VLDBVLDB-2015-BegumK14 #bound
Rare Time Series Motif Discovery from Unbounded Streams (NB, EJK), pp. 149–160.
VLDBVLDB-2015-DallachiesaPI14 #nearest neighbour #nondeterminism
Top-k Nearest Neighbor Search In Uncertain Data Series (MD, TP, IFI), pp. 13–24.
CHICHI-2014-AlbersCG #evaluation #visualisation
Task-driven evaluation of aggregation in time series visualization (DA, MC, MG), pp. 551–560.
CHICHI-2014-SchirraSB #twitter
Together alone: motivations for live-tweeting a television series (SS, HS, FB), pp. 2441–2450.
HCIHIMI-AS-2014-XingGLK #clustering
Decision Support Based on Time-Series Analytics: A Cluster Methodology (WX, RG, NL, TRK), pp. 217–225.
HCIHIMI-DE-2014-KobayashiS #corpus #topic
Finding Division Points for Time-Series Corpus Based on Topic Changes (HK, RS), pp. 364–372.
ICEISICEIS-v1-2014-AmaralCRGTS #approach #data mining #framework #image #mining
The SITSMining Framework — A Data Mining Approach for Satellite Image Time Series (BFA, DYTC, LASR, RRdVG, AJMT, EPMdS), pp. 225–232.
ICEISICEIS-v1-2014-ChinoGRTT #named #scalability
TrieMotif — A New and Efficient Method to Mine Frequent K-Motifs from Large Time Series (DYTC, RRdVG, LASR, CTJ, AJMT), pp. 60–69.
ICEISICEIS-v2-2014-SouzaVS #ontology
Domain Ontology for Time Series Provenance (LdS, MSMGV, MSS), pp. 217–224.
CIKMCIKM-2014-XuHCWHBA #framework #performance
A Demonstration of SearchonTS: An Efficient Pattern Search Framework for Time Series Data (XX, SH, YC, CW, IH, KB, MA), pp. 2015–2017.
ICMLICML-c1-2014-KhaleghiR #consistency #estimation
Asymptotically consistent estimation of the number of change points in highly dependent time series (AK, DR), pp. 539–547.
ICMLICML-c2-2014-Chapados #effectiveness #modelling
Effective Bayesian Modeling of Groups of Related Count Time Series (NC), pp. 1395–1403.
ICMLICML-c2-2014-JohnsonW #modelling #probability
Stochastic Variational Inference for Bayesian Time Series Models (MJ, ASW), pp. 1854–1862.
ICMLICML-c2-2014-WangY #crowdsourcing
Ensemble-Based Tracking: Aggregating Crowdsourced Structured Time Series Data (NW, DYY), pp. 1107–1115.
ICPRICPR-2014-BauckhageM #analysis #clustering #kernel #web
Kernel Archetypal Analysis for Clustering Web Search Frequency Time Series (CB, KM), pp. 1544–1549.
ICPRICPR-2014-ChevallierCA #estimation #image #metric #orthogonal #using
Computing Histogram of Tensor Images Using Orthogonal Series Density Estimation and Riemannian Metrics (EC, AC, JA), pp. 900–905.
ICPRICPR-2014-ContiFAAMCT #detection #distance #evaluation
Evaluation of Time Series Distance Functions in the Task of Detecting Remote Phenology Patterns (JCC, FAF, JA, BA, LPCM, LC, RdST), pp. 3126–3131.
ICPRICPR-2014-DamoulasHBGA #kernel #string
String Kernels for Complex Time-Series: Counting Targets from Sensed Movement (TD, JH, RB, CPG, AA), pp. 4429–4434.
ICPRICPR-2014-SousaSB #case study #classification #set
Time Series Transductive Classification on Imbalanced Data Sets: An Experimental Study (CARdS, VMAdS, GEAPAB), pp. 3780–3785.
ICPRICPR-2014-SouzaSB #classification
Extracting Texture Features for Time Series Classification (VMAdS, DFS, GEAPAB), pp. 1425–1430.
KDDKDD-2014-ChengB0 #approach #dependence #effectiveness #named
FBLG: a simple and effective approach for temporal dependence discovery from time series data (DC, MTB, YL), pp. 382–391.
KDDKDD-2014-ChiaS #mining #predict #scalability
Scalable noise mining in long-term electrocardiographic time-series to predict death following heart attacks (CCC, ZS), pp. 125–134.
KDDKDD-2014-GrabockaSWS #learning
Learning time-series shapelets (JG, NS, MW, LST), pp. 392–401.
KDDKDD-2014-LuoLLFDZW #correlation
Correlating events with time series for incident diagnosis (CL, JGL, QL, QF, RD, DZ, ZW), pp. 1583–1592.
MLDMMLDM-2014-Schafer #classification #preprocessor #towards
Towards Time Series Classification without Human Preprocessing (PS), pp. 228–242.
MLDMMLDM-2014-YuST #detection #modelling #realtime
Semi-supervised Time Series Modeling for Real-Time Flux Domain Detection on Passive DNS Traffic (BY, LS, MT), pp. 258–271.
SEKESEKE-2014-Otunba0 #approximate #detection #named
APT: Approximate Period Detection in Time Series (RO, JL), pp. 490–494.
SEKESEKE-2014-XuS #automation #effectiveness #petri net #testing
Effectiveness of Automated Function Testing with Petri Nets: A Series of Controlled Experiments (DX, NS), pp. 211–216.
REFSQREFSQ-2014-AbeleinP #communication #developer #scalability
State of Practice of User-Developer Communication in Large-Scale IT Projects — Results of an Expert Interview Series (UA, BP), pp. 95–111.
SACSAC-2014-SpiegelJA #classification #distance #performance
Fast time series classification under lucky time warping distance (SS, BJJ, SA), pp. 71–78.
CASECASE-2013-ZhouKZS #analysis
Causal analysis for non-stationary time series in sensor-rich smart buildings (YZ, ZK, LZ, CJS), pp. 593–598.
DATEDATE-2013-StergiouJ #dataset #optimisation
Optimizing BDDs for time-series dataset manipulation (SS, JJ), pp. 1018–1021.
ICDARICDAR-2013-GriechischML #analysis #online
Online Signature Analysis Based on Accelerometric and Gyroscopic Pens and Legendre Series (EG, MIM, ML), pp. 374–378.
VLDBVLDB-2013-0002GJ #correlation #markov #modelling #using
Travel Cost Inference from Sparse, Spatio-Temporally Correlated Time Series Using Markov Models (BY, CG, CSJ), pp. 769–780.
VLDBVLDB-2013-WangWPWH #adaptation #segmentation
A Data-adaptive and Dynamic Segmentation Index for Whole Matching on Time Series (YW, PW, JP, WW, SH), pp. 793–804.
VLDBVLDB-2014-EravciF13 #feedback
Diversity based Relevance Feedback for Time Series Search (BE, HF), pp. 109–120.
CHICHI-2013-FuchsFMBI #design #evaluation #multi
Evaluation of alternative glyph designs for time series data in a small multiple setting (JF, FF, FM, EB, PI), pp. 3237–3246.
CHICHI-2013-PerinVF #graph #interactive #multi #visualisation
Interactive horizon graphs: improving the compact visualization of multiple time series (CP, FV, JDF), pp. 3217–3226.
ICEISICEIS-v1-2013-AndradeRYS #data-driven #database #novel #similarity
A Novel Method for Similarity Search over Meteorological Time Series Data based on the Coulomb’s Law (CGdA, MXR, CAY, MTPS), pp. 209–216.
CIKMCIKM-2013-KimCHZRD #feedback #mining #modelling #topic
Mining causal topics in text data: iterative topic modeling with time series feedback (HDK, MC, MH, CZ, TAR, DD), pp. 885–890.
ICMLICML-c1-2013-WulsinFL #correlation #markov #parsing #process #using
Parsing epileptic events using a Markov switching process model for correlated time series (DW, EBF, BL), pp. 356–364.
ICMLICML-c2-2013-GaneshapillaiGL #learning
Learning Connections in Financial Time Series (GG, JVG, AL), pp. 109–117.
ICMLICML-c2-2013-HanL #estimation #matrix
Transition Matrix Estimation in High Dimensional Time Series (FH, HL), pp. 172–180.
ICMLICML-c3-2013-WuHG #modelling #multi
Dynamic Covariance Models for Multivariate Financial Time Series (YW, JMHL, ZG), pp. 558–566.
KDDKDD-2013-ChenHKB #learning #named
DTW-D: time series semi-supervised learning from a single example (YC, BH, EJK, GEAPAB), pp. 383–391.
KDDKDD-2013-ChenTTY #analysis #kernel #modelling #performance
Model-based kernel for efficient time series analysis (HC, FT, PT, XY), pp. 392–400.
KDDKDD-2013-HaoCZ0RK #learning #towards
Towards never-ending learning from time series streams (YH, YC, JZ, BH, TR, EJK), pp. 874–882.
KDDKDD-2013-RistanoskiLB
A time-dependent enhanced support vector machine for time series regression (GR, WL, JB), pp. 946–954.
MLDMMLDM-2013-VavreckaL #classification #feature model
EEG Feature Selection Based on Time Series Classification (MV, LL), pp. 520–527.
RecSysRecSys-2013-SchelterBSAM #distributed #matrix #pipes and filters #using
Distributed matrix factorization with mapreduce using a series of broadcast-joins (SS, CB, MS, AA, VM), pp. 281–284.
SIGIRSIGIR-2013-Soboroff #evaluation #interactive #student #tutorial
Building test collections: an interactive tutorial for students and others without their own evaluation conference series (IS), p. 1132.
SPLCSPLC-2013-TsuchiyaKWKFY #requirements #source code #traceability
Recovering traceability links between requirements and source code in the same series of software products (RT, TK, HW, MK, YF, KY), pp. 121–130.
ASEASE-2012-AminGC #approach #automation #linear #modelling
An automated approach to forecasting QoS attributes based on linear and non-linear time series modeling (AA, LG, AC), pp. 130–139.
CASECASE-2012-ChenZD #fourier #process
Mitigation of chatter instability in milling processes by active fourier series compensation (ZC, HTZ, HD), pp. 167–171.
HTHT-2012-NakajimaZIN #analysis #detection #scalability
Early detection of buzzwords based on large-scale time-series analysis of blog entries (SN, JZ, YI, RYN), pp. 275–284.
VLDBVLDB-2012-DallachiesaNMP #nondeterminism #similarity
Uncertain Time-Series Similarity: Return to the Basics (MD, BN, KM, TP), pp. 1662–1673.
STOCSTOC-2012-ChakrabartiFW #multi #network #problem
When the cut condition is enough: a complete characterization for multiflow problems in series-parallel networks (AC, LF, CW), pp. 19–26.
CIAACIAA-2012-LiuSGF #automaton #named #regular expression
SDFA: Series DFA for Memory-Efficient Regular Expression Matching (TL, YS, LG, BF), pp. 337–344.
CHICHI-2012-CorrellAFG
Comparing averages in time series data (MC, DA, SF, MG), pp. 1095–1104.
CIKMCIKM-2012-CandanRSW #named #scalability #set #visualisation
STFMap: query- and feature-driven visualization of large time series data sets (KSC, RR, MLS, XW), pp. 2743–2745.
CIKMCIKM-2012-KimZRDHCL #mining #named #topic
InCaToMi: integrative causal topic miner between textual and non-textual time series data (HDK, CZ, TAR, DD, MH, MC, CCL), pp. 2689–2691.
CIKMCIKM-2012-LiangZ #classification #performance
An efficient and simple under-sampling technique for imbalanced time series classification (GL, CZ), pp. 2339–2342.
CIKMCIKM-2012-OrangS #approach #correlation #nondeterminism #probability #query
A probabilistic approach to correlation queries in uncertain time series data (MO, NS), pp. 2229–2233.
ICMLICML-2012-JalaliS #dependence #graph #learning
Learning the Dependence Graph of Time Series with Latent Factors (AJ, SS), p. 83.
ICMLICML-2012-LiuL #modelling #multi #named
Sparse-GEV: Sparse Latent Space Model for Multivariate Extreme Value Time Serie Modeling (YL, MTB, HL), p. 156.
ICPRICPR-2012-HidoM #feature model #predict
Temporal feature selection for time-series prediction (SH, TM), pp. 3557–3560.
ICPRICPR-2012-LiuXFLMK #classification #modelling #multi #statistics
Statistical modeling and signal selection in multivariate time series pattern classification (RL, SX, CF, YwL, YLM, DSK), pp. 2853–2856.
ICPRICPR-2012-SuematsuH #process
Time series alignment with Gaussian processes (NS, AH), pp. 2355–2358.
KDDKDD-2012-BatalFHMH #detection #mining #multi
Mining recent temporal patterns for event detection in multivariate time series data (IB, DF, JH, FM, MH), pp. 280–288.
KDDKDD-2012-LinesDHB #classification
A shapelet transform for time series classification (JL, LMD, JH, AB), pp. 289–297.
KDDKDD-2012-RakthanmanonCMBWZZK #mining #sequence
Searching and mining trillions of time series subsequences under dynamic time warping (TR, BJLC, AM, GEAPAB, MBW, QZ, JZ, EJK), pp. 262–270.
KDIRKDIR-2012-SpiegelA #analysis #distance #invariant
An Order-invariant Time Series Distance Measure — Position on Recent Developments in Time Series Analysis (SS, SA), pp. 264–268.
SACSAC-2012-LiLXZR #named
TL-Tree: flash-optimized storage for time-series sensing data on sensor platforms (HL, DL, LX, GZ, KR), pp. 1565–1572.
SACSAC-2012-SchluterC #correlation #detection #markov #modelling #predict #using
Hidden markov model-based time series prediction using motifs for detecting inter-time-serial correlations (TS, SC), pp. 158–164.
SIGMODSIGMOD-2011-WangWW #semantics
Finding semantics in time series (PW, HW, WW), pp. 385–396.
CHICHI-2011-ZhaoCB #multi #named #navigation #using #visual notation
KronoMiner: using multi-foci navigation for the visual exploration of time-series data (JZ, FC, RB), pp. 1737–1746.
ICEISICEIS-J-2011-NganBL11a #framework #learning #monitoring #multi #query
An Event-Based Service Framework for Learning, Querying and Monitoring Multivariate Time Series (CKN, AB, JL), pp. 208–223.
ICEISICEIS-v1-2011-NiknafsSRR #analysis #comparative #predict
Comparative Analysis of Three Techniques for Predictions in Time Series Having Repetitive Patterns (AN, BS, MMR, GR), pp. 177–182.
ICEISICEIS-v2-2011-NganBL #framework #learning #monitoring #multi #query
A Service Framework for Learning, Querying and Monitoring Multivariate Time Series (CKN, AB, JL), pp. 92–101.
ICEISICEIS-v2-2011-TarsauliyaTS #network #search-based #using
Financial Time Series Forecast using Simulated Annealing and Threshold Acceptance Genetic BPA Neural Network (AT, RT, AS), pp. 172–177.
ICMLICML-2011-LiP #clustering #exclamation
Time Series Clustering: Complex is Simpler! (LL, BAP), pp. 185–192.
KDDKDD-2011-KashyapK #scalability
Scalable kNN search on vertically stored time series (SK, PK), pp. 1334–1342.
KDDKDD-2011-MueenKY #classification #named
Logical-shapelets: an expressive primitive for time series classification (AM, EJK, NEY), pp. 1154–1162.
KDDKDD-2011-TorgoO #2d #predict
2D-interval predictions for time series (LT, OO), pp. 787–794.
KDIRKDIR-2011-WongLZYFXKC #case study #segmentation
Time Series Segmentation as a Discovery Tool — A Case Study of the US and Japanese Financial Markets (JCW, GHTL, YZ, WSY, RPF, DYX, JLK, SAC), pp. 52–63.
MLDMMLDM-2011-ArmstrongD #database #scalability
Unsupervised Discovery of Motifs under Amplitude Scaling and Shifting in Time Series Databases (TA, ED), pp. 539–552.
SIGIRSIGIR-2011-Shokouhi #analysis #detection #query
Detecting seasonal queries by time-series analysis (MS), pp. 1171–1172.
SIGMODSIGMOD-2010-MueenNL #approximate #correlation #performance
Fast approximate correlation for massive time-series data (AM, SN, JL), pp. 171–182.
SIGMODSIGMOD-2010-RastogiN #distributed #encryption
Differentially private aggregation of distributed time-series with transformation and encryption (VR, SN), pp. 735–746.
VLDBVLDB-2010-LiPF #linear
Parsimonious Linear Fingerprinting for Time Series (LL, BAP, CF), pp. 385–396.
ICSMEICSM-2010-CanforaCCP #detection #empirical #logic #multi #using
Using multivariate time series and association rules to detect logical change coupling: An empirical study (GC, MC, LC, MDP), pp. 1–10.
CIKMCIKM-2010-ShangCSCH #towards
(k, P)-anonymity: towards pattern-preserving anonymity of time-series data (XS, KC, LS, GC, TH), pp. 1333–1336.
CIKMCIKM-2010-ZhaoAY #composition #nondeterminism #on the #set
On wavelet decomposition of uncertain time series data sets (YZ, CCA, PSY), pp. 129–138.
ICMLICML-2010-ChenW #modelling
Dynamical Products of Experts for Modeling Financial Time Series (YC, MW), pp. 207–214.
ICMLICML-2010-LiuNLL #analysis #graph #learning #relational
Learning Temporal Causal Graphs for Relational Time-Series Analysis (YL, ANM, ACL, YL), pp. 687–694.
ICPRICPR-2010-ChiuHW #clustering
AP-Based Consensus Clustering for Gene Expression Time Series (TYC, TCH, JSW), pp. 2512–2515.
ICPRICPR-2010-LewandowskiRMN #reduction
Temporal Extension of Laplacian Eigenmaps for Unsupervised Dimensionality Reduction of Time Series (ML, JMdR, DM, JCN), pp. 161–164.
ICPRICPR-2010-SeyedhosseiniPT #classification #image #network #parsing
Image Parsing with a Three-State Series Neural Network Classifier (MS, ARCP, TT), pp. 4508–4511.
ICPRICPR-2010-ZhangZZZ #classification #kernel #metric #using
Time Series Classification Using Support Vector Machine with Gaussian Elastic Metric Kernel (DZ, WZ, DZ, HZ), pp. 29–32.
KDDKDD-2010-MueenK #maintenance #online
Online discovery and maintenance of time series motifs (AM, EJK), pp. 1089–1098.
KDDKDD-2010-SarangiM #named #nondeterminism #similarity
DUST: a generalized notion of similarity between uncertain time series (SRS, KM), pp. 383–392.
SACSAC-2010-AraujoOS #hybrid #quantum
Hybrid evolutionary quantum inspired method to adjust time phase distortions in financial time series (RdAA, ALIdO, SCBS), pp. 1153–1154.
SACSAC-2010-LuoTLTDW #design #enterprise #evaluation #process #visualisation
Visualizing time-series data in processlines: design and evaluation of a process enterprise application (XL, FT, WL, DT, GD, HW), pp. 1165–1172.
SACSAC-2010-MarascuML #approximate #performance #set #streaming
A fast approximation strategy for summarizing a set of streaming time series (AM, FM, YL), pp. 1617–1621.
SACSAC-2010-RomaniAZCTT #algorithm #mining #named
CLEARMiner: a new algorithm for mining association patterns on heterogeneous time series from climate data (LASR, AMHdÁ, JZJ, RC, CTJ, AJMT), pp. 900–905.
ICDARICDAR-2009-GolubitskyW #multi #online #orthogonal #recognition
Online Recognition of Multi-Stroke Symbols with Orthogonal Series (OG, SMW), pp. 1265–1269.
VLDBVLDB-2009-AssentWKKS #database #performance #similarity
Anticipatory DTW for Efficient Similarity Search in Time Series Databases (IA, MW, RK, HK, TS), pp. 826–837.
VLDBVLDB-2009-ReevesLNZ #multi
Managing Massive Time Series Streams with MultiScale Compressed Trickles (GR, JL, SN, FZ), pp. 97–108.
DLTDLT-2009-Kirsten #commutative
The Support of a Recognizable Series over a Zero-Sum Free, Commutative Semiring Is Recognizable (DK), pp. 326–333.
LATALATA-2009-BaillyD #convergence
Absolute Convergence of Rational Series Is Semi-decidable (RB, FD), pp. 117–128.
LATALATA-2009-MatsubaraKBS #string
A Series of Run-Rich Strings (WM, KK, HB, AS), pp. 578–587.
CHICHI-2009-HeerKA #visual notation #visualisation
Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations (JH, NK, MA), pp. 1303–1312.
ICMLICML-2009-PetersJGS #detection
Detecting the direction of causal time series (JP, DJ, AG, BS), pp. 801–808.
KDDKDD-2009-ShibuyaHK #modelling #multi #quantifier
Causality quantification and its applications: structuring and modeling of multivariate time series (TS, TH, YK), pp. 787–796.
KDDKDD-2009-YeK #data mining #mining
Time series shapelets: a new primitive for data mining (LY, EJK), pp. 947–956.
MLDMMLDM-2009-NikovskiR #modelling #predict
Memory-Based Modeling of Seasonality for Prediction of Climatic Time Series (DN, GR), pp. 734–748.
MLDMMLDM-2009-ZhuFF #classification #privacy
Preserving Privacy in Time Series Data Classification by Discretization (YZ, YF, HF), pp. 53–67.
SACSAC-2009-FlorezL #video
Discovery of time series in video data through distribution of spatiotemporal gradients (OUF, SL), pp. 1816–1820.
DACDAC-2008-PangR #fixpoint #optimisation
Optimizing imprecise fixed-point arithmetic circuits specified by Taylor Series through arithmetic transform (YP, KR), pp. 397–402.
SIGMODSIGMOD-2008-AthitsosPPKG #approximate #sequence
Approximate embedding-based subsequence matching of time series (VA, PP, MP, GK, DG), pp. 365–378.
VLDBVLDB-2008-DingTSWK #comparison #distance #metric #mining #query
Querying and mining of time series data: experimental comparison of representations and distance measures (HD, GT, PS, XW, EJK), pp. 1542–1552.
MSRMSR-2008-SiyCS #challenge #developer #segmentation #using
Summarizing developer work history using time series segmentation: challenge report (HPS, PC, MS), pp. 137–140.
CIAACIAA-2008-Maletti
Tree-Series-to-Tree-Series Transformations (AM), pp. 132–140.
ICALPICALP-B-2008-Mathissen #algebra #logic #word
Weighted Logics for Nested Words and Algebraic Formal Power Series (CM), pp. 221–232.
CHICHI-2008-McLachlanMKN #interactive #named #visual notation
LiveRAC: interactive visual exploration of system management time-series data (PM, TM, EK, SCN), pp. 1483–1492.
ICEISICEIS-AIDSS-2008-NguyenG #approach #evolution #mining #multi
Rule Evolution Approach for Mining Multivariate Time Series Data (VAN, VG), pp. 19–26.
CIKMCIKM-2008-NguyenS #analysis #correlation #dataset #performance
Fast correlation analysis on time series datasets (PN, NS), pp. 787–796.
ECIRECIR-2008-EuachongprasitR #multi #normalisation #performance #retrieval #scalability
Efficient Multimedia Time Series Data Retrieval Under Uniform Scaling and Normalisation (WE, CAR), pp. 506–513.
ICMLICML-2008-LuLHE #framework #kernel
A reproducing kernel Hilbert space framework for pairwise time series distances (ZL, TKL, YH, DE), pp. 624–631.
ICPRICPR-2008-HautamakiNF #approximate #clustering #prototype
Time-series clustering by approximate prototypes (VH, PN, PF), pp. 1–4.
ICPRICPR-2008-KazuiMMF #detection #matrix #using
Incoherent motion detection using a time-series Gram matrix feature (MK, MM, SM, HF), pp. 1–5.
KDDKDD-2008-ChengT #learning
Semi-supervised learning with data calibration for long-term time series forecasting (HC, PNT), pp. 133–141.
KDDKDD-2008-ShiehK #mining #named
iSAX: indexing and mining terabyte sized time series (JS, EJK), pp. 623–631.
ICSEICSE-2008-RiccaPTTCV #evolution
Are fit tables really talking?: a series of experiments to understand whether fit tables are useful during evolution tasks (FR, MDP, MT, PT, MC, CAV), pp. 361–370.
SIGMODSIGMOD-2007-MorseP #performance #similarity
An efficient and accurate method for evaluating time series similarity (MDM, JMP), pp. 569–580.
VLDBVLDB-2007-HanLMJ #database #sequence
Ranked Subsequence Matching in Time-Series Databases (WSH, JL, YSM, HJ), pp. 423–434.
VLDBVLDB-2007-LiH #approximate #mining #multi
Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data (XL, JH), pp. 447–458.
VLDBVLDB-2007-PapadimitriouLKY #privacy
Time Series Compressibility and Privacy (SP, FL, GK, PSY), pp. 459–470.
ICSMEICSM-2007-SiyCRS #developer #segmentation #version control
Discovering Dynamic Developer Relationships from Software Version Histories by Time Series Segmentation (HPS, PC, DJR, MS), pp. 415–424.
MSRMSR-2007-HerraizGR07a #analysis #eclipse #using
Forecasting the Number of Changes in Eclipse Using Time Series Analysis (IH, JMGB, GR), p. 32.
WCREWCRE-2007-RatzingerGP #assessment #evolution #quality
Quality Assessment Based on Attribute Series of Software Evolution (JR, HG, MP), pp. 80–89.
STOCSTOC-2007-BrinkmanKL #graph #random #reduction
Vertex cuts, random walks, and dimension reduction in series-parallel graphs (BB, AK, JRL), pp. 621–630.
LATALATA-2007-Martugin #automaton
A series of slowly synchronizable automata with a zero state over a small alphabet (PVM), pp. 391–402.
CIKMCIKM-2007-YangQZGL #analysis #graph #using #web
Link analysis using time series of web graphs (LY, LQ, YPZ, BG, TYL), pp. 1011–1014.
CIKMCIKM-2007-ZhangYGY #adaptation #analysis #correlation #representation
Boolean representation based data-adaptive correlation analysis over time series streams (TZ, DY, YG, GY), pp. 203–212.
ICMLICML-2007-XuanM #dependence #modelling #multi
Modeling changing dependency structure in multivariate time series (XX, KPM), pp. 1055–1062.
KDDKDD-2007-YankovKMCZ #detection #scalability
Detecting time series motifs under uniform scaling (DY, EJK, JM, BYcC, VBZ), pp. 844–853.
ICSEICSE-2007-CarverKSP #case study #development
Software Development Environments for Scientific and Engineering Software: A Series of Case Studies (JCC, RPK, SES, DEP), pp. 550–559.
SIGMODSIGMOD-2006-PapadimitriouY #multi
Optimal multi-scale patterns in time series streams (SP, PSY), pp. 647–658.
VLDBVLDB-2006-Keogh #database #mining #scalability
A Decade of Progress in Indexing and Mining Large Time Series Databases (EJK), p. 1268.
DLTDLT-2006-Maletti #revisited
Hierarchies of Tree Series Transformations Revisited (AM), pp. 215–225.
CIKMCIKM-2006-GoldinMN #algorithm #clustering #distance #sequence
In search of meaning for time series subsequence clustering: matching algorithms based on a new distance measure (DQG, RM, GN), pp. 347–356.
CIKMCIKM-2006-GrecoRT #effectiveness #performance #similarity
Effective and efficient similarity search in time series (SG, MR, AT), pp. 808–809.
CIKMCIKM-2006-LiuJK #clustering #query
Measuring the meaning in time series clustering of text search queries (BL, RJ, KLK), pp. 836–837.
ICMLICML-2006-XiKSWR #classification #performance #reduction #using
Fast time series classification using numerosity reduction (XX, EJK, CRS, LW, CAR), pp. 1033–1040.
ICPRICPR-v3-2006-HuRH #approach #clustering #robust
An Interweaved HMM/DTW Approach to Robust Time Series Clustering (JH, BKR, LH), pp. 145–148.
ICPRICPR-v4-2006-BrandtZ #robust
Robust Alignment of Transmission Electron Microscope Tilt Series (SSB, UZ), pp. 683–686.
KDDKDD-2006-Morchen #algorithm #mining
Algorithms for time series knowledge mining (FM), pp. 668–673.
KDDKDD-2006-WeiK #classification
Semi-supervised time series classification (LW, EJK), pp. 748–753.
KDDKDD-2006-ZhangCFM #detection #recommendation
Attack detection in time series for recommender systems (SZ, AC, JF, FM), pp. 809–814.
ICSEICSE-2006-LeeKC #development
A series of development methodologies for a variety of systems in Korea (JL, JSK, JHC), pp. 612–615.
DATEDATE-2005-MartensG #integration #orthogonal #polynomial #simulation #using
Time-Domain Simulation of Sampled Weakly Nonlinear Systems Using Analytical Integration and Orthogonal Polynomial Series (EM, GGEG), pp. 120–125.
HTHT-2005-ToyodaK #evolution #graph #visualisation #web
A system for visualizing and analyzing the evolution of the web with a time series of graphs (MT, MK), pp. 151–160.
SIGMODSIGMOD-2005-WuSSJSK #sequence
Subsequence Matching on Structured Time Series Data (HW, BS, GCS, SBJ, HS, DRK), pp. 682–693.
VLDBVLDB-2005-FuKLR #query #scalability
Scaling and Time Warping in Time Series Querying (AWCF, EJK, LYHL, C(R), pp. 649–660.
VLDBVLDB-2005-PapadimitriouSF #multi #streaming
Streaming Pattern Discovery in Multiple Time-Series (SP, JS, CF), pp. 697–708.
DLTDLT-J-2004-Maletti05 #automaton #transducer
Relating tree series transducers and weighted tree automata (AM), pp. 723–741.
DLTDLT-2005-Maletti #power of #transducer
The Power of Tree Series Transducers of Type I and II (AM), pp. 338–349.
ICEISICEIS-v2-2005-CuellarDJ #network #predict #problem #programming
An Application of Non-Linear Programming to Train Recurrent Neural Networks in Time Series Prediction Problems (MPC, MD, MdCPJ), pp. 35–42.
ICEISICEIS-v2-2005-NilssonFX #classification #using
Clinical Decision Support by Time Series Classification Using Wavelets (MN, PF, NX), pp. 169–175.
CIKMCIKM-2005-FuCTLN #incremental #visualisation
Incremental stock time series data delivery and visualization (TCF, FLC, PyT, RWPL, CmN), pp. 279–280.
CIKMCIKM-2005-LuoJH #estimation
Applying cosine series to join size estimation (CL, ZJ, WCH), pp. 227–228.
ICMLICML-2005-SinghPGBB #analysis #learning
Active learning for sampling in time-series experiments with application to gene expression analysis (RS, NP, DKG, BB, ZBJ), pp. 832–839.
KDDKDD-2005-ColeSZ #correlation #performance
Fast window correlations over uncooperative time series (RC, DS, XZ), pp. 743–749.
KDDKDD-2005-MorchenU #information management #optimisation
Optimizing time series discretization for knowledge discovery (FM, AU), pp. 660–665.
MLDMMLDM-2005-BunkeDIK #analysis #graph #learning #predict
Analysis of Time Series of Graphs: Prediction of Node Presence by Means of Decision Tree Learning (HB, PJD, CI, MK), pp. 366–375.
MLDMMLDM-2005-HayashiMS #classification
Embedding Time Series Data for Classification (AH, YM, NS), pp. 356–365.
SACSAC-2005-FalcoTCP #approach #induction #programming #search-based
Inductive inference of chaotic series by Genetic Programming: a Solomonoff-based approach (IDF, ET, ADC, AP), pp. 957–958.
SACSAC-2005-GaoWW #evaluation #quality #streaming
Quality-driven evaluation of trigger conditions on streaming time series (LG, MW, XSW), pp. 563–567.
SACSAC-2005-KimJ #performance #sequence
Performance bottleneck in time-series subsequence matching (SWK, BSJ), pp. 469–473.
SACSAC-2005-KimKS #database #optimisation #sequence
Optimization of subsequence matching under time warping in time-series databases (MSK, SWK, MS), pp. 581–586.
SIGMODSIGMOD-2004-LernerSWZZ #algorithm #biology #music #performance #physics
Fast Algorithms for Time Series with applications to Finance, Physics, Music, Biology, and other Suspects (AL, DS, ZW, XZ, YZ), pp. 965–968.
VLDBVLDB-2004-LinKLLN #database #mining #monitoring #named #visual notation
VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases (JL, EJK, SL, JPL, DMN), pp. 1269–1272.
CIAACIAA-2004-Borchardt #transducer
Code Selection by Tree Series Transducers (BB), pp. 57–67.
DLTDLT-2004-ChoffrutGL #on the
On the Maximum Coefficients of Rational Formal Series in Commuting Variables (CC, MG, VL), pp. 114–126.
DLTDLT-2004-Maletti #automaton #transducer
Relating Tree Series Transducers and Weighted Tree Automata (AM), pp. 321–333.
ICEISICEIS-v2-2004-CamargoFPS #network #tool support
Neural Network and Time Series as Tools for Sales Forecasting (MC, WPF, MP, AS), pp. 476–478.
ICEISICEIS-v2-2004-KooptiwootS #mining #set #using
Mining the Relationships in the Form of the Predisposing Factors and Coincident Factors Among Numerical Dynamic Attributes in Time Series Data Set by Using the Combination of Some Existing Techniques (SK, MAS), pp. 327–334.
ICEISICEIS-v2-2004-KooptiwootS04a #idea #mining #set #using
Mining the Relationships in the Form of Predisposing Factor and Coincident Factor in Time Series Data Set by Using the Combination of some Existing Ideas with a new Idea from the Fact in the Chemical Reaction (SK, MAS), pp. 531–534.
ICEISICEIS-v2-2004-UdechukwuBA #framework #mining #performance
An Efficient Framework for Iterative Time-Series Trend Mining (AU, KB, RA), pp. 130–137.
CIKMCIKM-2004-MegalooikonomouLW #analysis #database #performance #reduction #similarity
A dimensionality reduction technique for efficient similarity analysis of time series databases (VM, GL, QW), pp. 160–161.
ICPRICPR-v3-2004-JiaQD #detection #markov #modelling #online
An Advanced Segmental Semi-Markov Model Based Online Series Pattern Detection (SJ, YQ, GD), pp. 634–637.
ICPRICPR-v4-2004-LaiY #algorithm #fault #network #predict
Successive-Least-Squares Error Algorithm on Minimum Description Length Neural Networks for Time Series Prediction (YNL, SYY), pp. 609–612.
KDDKDD-2004-AiroldiF #network
Recovering latent time-series from their observed sums: network tomography with particle filters (EA, CF), pp. 30–39.
KDDKDD-2004-BagnallJ #clustering #modelling
Clustering time series from ARMA models with clipped data (AJB, GJJ), pp. 49–58.
KDDKDD-2004-LinKLLN #mining #monitoring #visual notation
Visually mining and monitoring massive time series (JL, EJK, SL, JPL, DMN), pp. 460–469.
DATEDATE-2003-YevtushenkoVBPS #equation
Equisolvability of Series vs. Controller’s Topology in Synchronous Language Equations (NY, TV, RKB, AP, ALSV), pp. 11154–11155.
HTHT-2003-ToyodaK #community #evolution #web
Extracting evolution of web communities from a series of web archives (MT, MK), pp. 28–37.
DLTDLT-2003-Borchardt #theorem
The Myhill-Nerode Theorem for Recognizable Tree Series (BB), pp. 146–158.
DLTDLT-2003-BouillardM #generative
Generating Series of the Trace Group (AB, JM), pp. 159–170.
ICALPICALP-2003-DrosteK
Skew and Infinitary Formal Power Series (MD, DK), pp. 426–438.
ICMLICML-2003-LangleyGBS #induction #modelling #process #robust
Robust Induction of Process Models from Time-Series Data (PL, DG, SDB, KS), pp. 432–439.
ICMLICML-2003-YamadaSYT #data-driven #database #induction #standard
Decision-tree Induction from Time-series Data Based on a Standard-example Split Test (YY, ES, HY, KT), pp. 840–847.
KDDKDD-2003-ChiuKL #probability
Probabilistic discovery of time series motifs (BYcC, EJK, SL), pp. 493–498.
KDDKDD-2003-JiangPZ #interactive
Interactive exploration of coherent patterns in time-series gene expression data (DJ, JP, AZ), pp. 565–570.
KDDKDD-2003-SripadaRHY #generative #summary #using
Generating English summaries of time series data using the Gricean maxims (SS, ER, JH, JY), pp. 187–196.
KDDKDD-2003-VlachosHGK #distance #metric #multi
Indexing multi-dimensional time-series with support for multiple distance measures (MV, MH, DG, EJK), pp. 216–225.
MLDMMLDM-2003-TanakaU #analysis #component #multi #principle #using
Discover Motifs in Multi-dimensional Time-Series Using the Principal Component Analysis and the MDL Principle (YT, KU), pp. 252–265.
CAVCAV-2003-Drusinsky #monitoring
Monitoring Temporal Rules Combined with Time Series (DD), pp. 114–117.
DATEDATE-2002-GoffioulWVD #analysis #approach #architecture #using
Analysis of Nonlinearities in RF Front-End Architectures Using a Modified Volterra Series Approach (MG, PW, GV, SD), pp. 352–356.
SIGMODSIGMOD-2002-GaoW #query #similarity #streaming
Continually evaluating similarity-based pattern queries on a streaming time series (LG, XSW), pp. 370–381.
SIGMODSIGMOD-2002-MoonWH #database #sequence
General match: a subsequence matching method in time-series databases based on generalized windows (YSM, KYW, WSH), pp. 382–393.
VLDBVLDB-2002-ChenHWW #analysis #data type #multi
Multi-Dimensional Regression Analysis of Time-Series Data Streams (YC, GD, JH, BWW, JW), pp. 323–334.
ICSMEICSM-2002-FuentetajaB #evolution #perspective
Software Evolution from a Time-Series Perspective (EF, DJB), pp. 226–229.
CAiSECAiSE-2002-LeeKL #data-driven #database #distance #performance #similarity
Efficient Similarity Search for Time Series Data Based on the Minimum Distance (SL, DK, SL), pp. 377–391.
CIKMCIKM-2002-GaoYW #nearest neighbour #query #streaming
Evaluating continuous nearest neighbor queries for streaming time series via pre-fetching (LG, ZY, XSW), pp. 485–492.
CIKMCIKM-2002-MotoyoshiMW #mining
Mining temporal classes from time series data (MM, TM, KW), pp. 493–498.
KDDKDD-2002-JinLS #similarity
Similarity measure based on partial information of time series (XJ, YL, CS), pp. 544–549.
KDDKDD-2002-KeoghK #benchmark #bibliography #data mining #empirical #metric #mining #on the
On the need for time series data mining benchmarks: a survey and empirical demonstration (EJK, SK), pp. 102–111.
KDDKDD-2002-KeoghLC #database #linear
Finding surprising patterns in a time series database in linear time and space (EJK, SL, BYcC), pp. 550–556.
KDDKDD-2002-YamanishiT #detection #framework
A unifying framework for detecting outliers and change points from non-stationary time series data (KY, JiT), pp. 676–681.
TOOLSTOOLS-USA-2002-PatelPS #object-oriented
Object Oriented Extension to Time Series Model (DP, SP, PS), pp. 159–171.
RERE-2002-HardtMB #development #requirements
Integrating ECUs in Vehicles — Requirements Engineering in Series Development (MH, RM, JB), pp. 227–236.
SACSAC-2002-KimYPK #database #retrieval #sequence
Shape-based retrieval of similar subsequences in time-series databases (SWK, JY, SP, THK), pp. 438–445.
ICSMEICSM-2001-AntoniolCPM #evolution #modelling
Modeling Clones Evolution through Time Series (GA, GC, MDP, EM), pp. 273–280.
STOCSTOC-2001-Kosaraju #graph #parallel
Euler paths in series parallel graphs (SRK), pp. 237–240.
DLTDLT-2001-EsikN #automaton
Automata on Series-Parallel Biposets (, ZLN), pp. 217–227.
ICALPICALP-2001-DrosteZ
Rational Transformations of Formal Power Series (MD, GQZ), pp. 555–566.
CIKMCIKM-2001-PollyW #feature model #pattern matching #performance #robust
Efficient and Robust Feature Extraction and Pattern Matching of Time Series by a Lattice Structure (WPMP, MHW), pp. 271–278.
ICMLICML-2001-SarkarL #fuzzy #similarity
Application of Fuzzy Similarity-Based Fractal Dimensions to Characterize Medical Time Series (MS, TYL), pp. 465–472.
ICMLICML-2001-SebastianiR #clustering
Clustering Continuous Time Series (PS, MR), pp. 497–504.
VLDBVLDB-2000-KoudasIM #identification #roadmap #set #sketching #using
Identifying Representative Trends in Massive Time Series Data Sets Using Sketches (PI, NK, SM), pp. 363–372.
ICALPICALP-2000-Kuske #infinity #logic
Infinite Series-Parallel Posets: Logic and Languages (DK), pp. 648–662.
CIKMCIKM-2000-LohKW #approach #database #normalisation #sequence
Index Interpolation: An Approach to Subsequence Matching Supporting Normalization Transform in Time-Series Databases (WKL, SWK, KYW), pp. 480–487.
CIKMCIKM-2000-WuAA00a #comparison #database #similarity
A Comparison of DFT and DWT based Similarity Search in Time-Series Databases (YLW, DA, AEA), pp. 488–495.
ICPRICPR-v2-2000-PolickerG #algorithm #clustering #fuzzy #predict
A New Algorithm for Time Series Prediction by Temporal Fuzzy Clustering (SP, ABG), pp. 2728–2731.
KDDKDD-2000-Caraca-ValenteL
Discovering similar patterns in time series (JPCV, ILC), pp. 497–505.
KDDKDD-2000-GeS #markov #pattern matching
Deformable Markov model templates for time-series pattern matching (XG, PS), pp. 81–90.
KDDKDD-2000-YangWY #mining
Mining asynchronous periodic patterns in time series data (JY, WW, PSY), pp. 275–279.
PODSPODS-1999-ChuW #performance #scalability
Fast Time-Series Searching with Scaling and Shifting (KKWC, MHW), pp. 237–248.
VLDBVLDB-1999-KoudasMJ #database #mining
Mining Deviants in a Time Series Database (HVJ, NK, SM), pp. 102–113.
DLTDLT-1999-Petre #on the
On semilinearity in formal power series (IP), pp. 220–231.
ICALPICALP-1999-Rutten #automaton #induction
Automata, Power Series, and Coinduction: Taking Input Derivatives Seriously (JJMMR), pp. 645–654.
ICEISICEIS-1999-Habrant #database #learning #network #predict #search-based
Structure Learning of Bayesian Networks from Databases by Genetic Algorithms-Application to Time Series Prediction in Finance (JH), pp. 225–231.
ICMLICML-1999-BontempiBB #learning #predict
Local Learning for Iterated Time-Series Prediction (GB, MB, HB), pp. 32–38.
ICMLICML-1999-Kadous #learning #multi
Learning Comprehensible Descriptions of Multivariate Time Series (MWK), pp. 454–463.
KDDKDD-1999-GuralnikS #detection
Event Detection from Time Series Data (VG, JS), pp. 33–42.
KDDKDD-1999-HuangY #adaptation #query
Adaptive Query Processing for Time-Series Data (YWH, PSY), pp. 282–286.
KDDKDD-1999-Oates #clustering #identification #multi #sequence
Identifying Distinctive Subsequences in Multivariate Time Series by Clustering (TO), pp. 322–326.
SIGIRSIGIR-1999-KeoghP #feedback #retrieval
Relevance Feedback Retrieval of Time Series Data (EJK, MJP), pp. 183–190.
CIKMCIKM-1998-QuWW #multi #performance
Supporting Fast Search in Time Series for Movement Patterns in Multiple Scales (YQ, CW, XSW), pp. 251–258.
ICPRICPR-1998-SinghS #pattern matching
A pattern matching tool for time-series forecasting (SS, ES), pp. 103–105.
ICPRICPR-1998-WuSK #gesture #image #recognition
Spotting recognition of head gestures from color image series (HW, TS, HK), pp. 83–85.
KDDKDD-1998-DasLMRS
Rule Discovery from Time Series (GD, KIL, HM, GR, PS), pp. 16–22.
KDDKDD-1998-KeoghP #classification #clustering #feedback #performance #representation
An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback (EJK, MJP), pp. 239–243.
KDDKDD-1998-RaoRC #adaptation #multi
Time Series Forecasting from High-Dimensional Data with Multiple Adaptive Layers (RBR, SR, FC), pp. 319–323.
SIGMODSIGMOD-1997-RafieiM #query #similarity
Similarity-Based Queries for Time Series Data (DR, AOM), pp. 13–25.
DLTDLT-1997-Kuich97a
Formal Power Series over Trees (WK), pp. 61–101.
ICALPICALP-1997-DrosteG #on the
On Recognizable and Rational Formal Power Series in Partially Commuting Variables (MD, PG), pp. 682–692.
KDDKDD-1997-DeCoste #behaviour #mining #multi
Mining Multivariate Time-Series Sensor Data to Discover Behavior Envelopes (DD), pp. 151–154.
KDDKDD-1997-KeoghS #approach #database #pattern matching #performance #probability
A Probabilistic Approach to Fast Pattern Matching in Time Series Databases (EJK, PS), pp. 24–30.
RTARTA-1997-BechetGR #axiom #partial order
A Complete Axiomatisation for the Inclusion of Series-Parallel Partial Orders (DB, PdG, CR), pp. 230–240.
CIAAWIA-1996-BoneDGM #automaton #finite
Time Series Forecasting by Finite-State Automata (RB, CD, AG, DM), pp. 26–34.
KDDAKDDM-1996-BerndtC #approach #programming
Finding Patterns in Time Series: A Dynamic Programming Approach (DJB, JC), pp. 229–248.
ICPRICPR-1996-Chernov #theorem
Tauber theorems for Dirichlet series and fractals (VMC), pp. 656–661.
SIGMODSIGMOD-1995-DreyerDS #using
Using the CALANDA Time Series Management System (WD, AKD, DS), p. 489.
VLDBVLDB-1995-AgrawalLSS #database #performance #scalability #similarity
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases (RA, KIL, HSS, KS), pp. 490–501.
DACDAC-1994-Nishimukai
Hitachi-PA/50, SH Series Microcontroller (TN), pp. 592–593.
SIGMODSIGMOD-1994-FaloutsosRM #database #performance #sequence
Fast Subsequence Matching in Time-Series Databases (CF, MR, YM), pp. 419–429.
KDDKDD-1994-BerndtC #using
Using Dynamic Time Warping to Find Patterns in Time Series (DJB, JC), pp. 359–370.
ICSMECSM-1993-Winston #standard
Impact of the ISO 9000 Standard Series (ABW), pp. 231–232.
DLTDLT-1993-Honkala #on the
On Lindenmayerian Series in Complete Semirings (JH), pp. 179–192.
DLTDLT-1993-Kuich
Lindenmayer Systems Generalized to Formal Power Series and Their Growth Functions (WK), pp. 171–178.
ICALPICALP-1993-Dumas #algebra #aspect-oriented
Algebraic Aspects of B-regular Series (PD), pp. 457–468.
ICALPICALP-1992-Krob #decidability #multi #problem #similarity
The Equality Problem for Rational Series with Multiplicities in the Tropical Semiring is Undecidable (DK), pp. 101–112.
CADECADE-1992-WalshNB #proving #theorem proving
The Use of Proof Plans to Sum Series (TW, AN, AB), pp. 325–339.
ICALPICALP-1988-LingasS #algorithm #graph #morphism #polynomial
A Polynomial-Time Algorithm for Subgraph Isomorphism of Two-Connected Series-Parallel Graphs (AL, MMS), pp. 394–409.
PLDIPLDI-1987-Waters #performance
Efficient interpretation of synchronizable series expressions (RCW), pp. 74–85.
ICALPICALP-1987-Muller #complexity
Uniform Computational Complexity of Taylor Series (NTM), pp. 435–444.
OOPSLAOOPSLA-1987-KerrP #analysis #object-oriented #programming
Use of Object-Oriented Programming in a Time Series Analysis System (RKK, DBP), pp. 1–10.
SIGMODSIGMOD-1986-GardarinM #database #evaluation #logic programming #recursion #source code
Evaluation of Database Recursive Logic Programs as Recurrent Function Series (GG, CdM), pp. 177–186.
DACDAC-1985-OgiharaSM #automation #generative #named #parametricity #testing
PATEGE: an automatic DC parametric test generation system for series gated ECL circuits (TO, SS, SM), pp. 212–218.
STOCSTOC-1979-ValdesTL #graph #parallel #recognition
The recognition of Series Parallel digraphs (JV, RET, ELL), pp. 1–12.
STOCSTOC-1973-Teitelbaum #algebra #analysis #evaluation #fault
Context-Free Error Analysis by Evaluation of Algebraic Power Series (RT), pp. 196–199.
STOCSTOC-1971-Stanat #formal method
Formal Languages and Power Series (DFS), pp. 1–11.
DACDAC-1970-Hayashi #automation #design
FACOM 230-series computer design automation system (TH), pp. 230–242.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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