Time Series

Summary

  • 172 Companies
  • 344 Patents
  • 3 085 Use Cases
  • 886 Case Studies
  • 32 605 Science Papers
  • $65 583 188 Total Funding

Companies

#Organisation NameIndustriesHeadquarterDescriptionFounded YearCompany TypeNum of Employees
1
Software
San Francisco, California
InfluxData is the creator of InfluxDB, the open source time series database. Our technology is purpose-built to handle the massive volumes of time-stamped data produced by IoT devices, applications, networks, containers and computers. We are on a mission to help developers and organizations, such as Cisco, IBM, PayPal, and Tesla, store and analyze real-time data, empowering them to build transformative monitoring, analytics, and IoT applications quicker and to scale. InfluxData is headquartered in San Francisco with a workforce distributed throughout the U.S. and across Europe. For more information, visit www.influxdata.com and follow us @InfluxDB.
2012
Privately Held
259
2
Internet
New York, NY
Grafana Labs is the company behind Grafana, the leading open source software for visualizing time series data. Grafana Labs helps users get the most out of Grafana, enabling them to take control of their unified monitoring and avoid vendor lock in and the spiraling costs of closed solutions.
2014
Privately Held
783
3
Animation
Vancouver, B.C.
What began as a small family business making hand-crafted animation has evolved over the past 30 years into a digital studio spanning two state-of-the-art facilities. Bardel has become a trusted name in the industry, making series productions for a diverse slate of collaborators: Nickelodeon, Disney, Cartoon Network, DreamWorks, Rovio, Matel and Warner Bros. Our current productions include Teen Titans Go!, Rick & Morty, 44 Cats and several top secret projects we can't mention yet!. Bardel is also working on feature films, prime-time series, kids, and preschool television, as well as projects for SVOD and VOD platforms. Check out our job postings and apply today! http://www.bardel.ca/careers
1987
Privately Held
480
4
Entertainment
Knoxville, Tennessee
Jupiter Entertainment has an enviable track record of creating successful, highly formatted series with outstanding ratings and strong renewals. From critically- acclaimed series like the explosive docusoap Sons of Guns and true crime powerhouse Snapped to established series like Biography and Modern Marvels, Jupiter has been successful in producing a wide variety of genres and formats by focusing on the timeless ingredients of great storytelling, compelling characters and high production values. Over the past 18 years, Jupiter has produced hit prime-time series and specials for A&E, Discovery, History, Investigation Discovery, Oxygen, TruTV, Fuse, and Animal Planet. Members of Jupiter’s creative team have also produced kid’s programming for Nickelodeon, daytime how-to shows for HGTV, a music-based series for TNN and even a sports entertainment property for CBS Cable – the re-launch of the American classic, Roller Derby. The company’s strong foundation, combined with its ability to craft stories that connect to their target audience, has Jupiter Entertainment poised to continue its success within the television industry as it explores alternate distribution platforms beyond.
1996
Privately Held
295
5
Information Technology
Menlo Park, California
Move from Analyzing Data to Automating Actions. VIA AIOps, a next generation application for fault, performance and change management accelerates the detection and resolution of service-impacting issues across the technology stack. VIA leverages data science to reduce alarm noise, detect earlier, uncover the root cause and prescribe actions. VIA is a non-disruptive value multiplier to existing monitoring tools and processes by integrating with and correlating across already deployed systems. Across service layers, VIA uses advanced analytics, AI, and machine learning to move from noise reduction and fault detection to service assurance and predictive analytics. Ingesting and enriching both asynchronous events and time series data in real time, VIA addresses fault and performance management within a single AIOps application. VIA creates intraday seasonal baselines to define faults faster than simple threshold boundary conditions. Learning application and service dependencies, VIA creates rich metadata for use in correlation and affinity analysis that accelerate diagnostics. AI-powered analysis determines if events and signals across operating silos are related and whether they should be treated as a single incident or separately. Symptoms are distinguished from probable cause and the impacted populations defined. Vitria is a company with a long history of innovation. The company celebrates twenty plus years of providing solutions that have transformed business process management and application delivery across the distributed enterprise. Vitria has 20+ years of experience in model-driven compute environments, process automation, complex event processing and big data analytics. Learn more by visiting www.vitria.com
1994
Privately Held
140
6
Financial Services
New York, NY
Haver Analytics is the premier provider of time series data for the global strategy and research community. We maintain over 200 Economic and Financial databases from more than 1350 government and private sources. Coverage includes great detail for the advanced and developing economies across the globe, as well as key third party and forecast data provided through our strategic partnerships. Upon each release, data is delivered quickly and accurately to clients via Haver’s custom DLX software, which is optimized for charting and analysis. We pride ourselves on the quality of the data and ease of access, as well as the speed at which it is updated and supported. Founded in 1978, Haver is headquartered in New York City with offices in London, Singapore, and Tokyo.
1978
Privately Held
137
7
Information Technology
Hyderabad, Andhra Pradesh
DEFTeam provides subscription support, consulting and System Integration services for an end-to-end custom built Data Warehouse, Business Intelligence and Predictive Analytics solution based upon business requirements. We utilize open source and in-memory BI and analytical tools to architect a solution that utilizes “best of breed” features of various tools and tames structured and unstructured Big data to provide a lower TCO but a feature rich solution on Cloud as well as in house. We work with Multiple tools like Pentaho, Jaspersoft, Revolution Analytics, Actian vectorwise, Microstrategy to provide best bit solution to clients. We write ETL (Extract Transform & Load) to populate Data Marts, design Data Models, Develop Reports, Dashboards, Mashborads, OLAP cubes and Adhoc Reporting/Analysis, write custom Plug-ins to enhance platform to cater to specific requirements, create Predictive models for forecasting, time series, trends analysis, convert proprietary BI solutions to open source solutions, provide Single Sign On integration and embedding with other applications. We offer offshore centric delivery model that leverages labor arbitrage which can be tailored to achieve even 100% offshore delivery. DEFTeam is a Red Herring Global 100 company for 2011. To get more information, pl. visit www.defteam.com
2002
Privately Held
118
8
-
Mountain View, California
JP Research, Inc. is a leading US statistical and engineering research firm providing research and a broad range of litigation support services in the fields of automotive and consumer product safety. The company integrates advanced statistics, data analytics and engineering (mechanical, automotive, design, and bioengineering) disciplines to address global safety research problems. In bringing together highly specialized technical fields of expertise, JP Research’s approaches to problem solving frequently set the bar for future research. JP Research founded an international consortium to support a Road Accident Sampling System for India (RASSI), and has established a fully incorporated company, JP Research India, Pvt., Ltd., to pursue automotive safety research, accident data collection and crash investigation in India. Specialties Statistical Modeling, 
Probability & Risk Analysis,
 Class Action
, Comparative Risk Assessment, 
Claims, Consumer Complaints Analyses,
 Statistical Significance
, Failure & Reliability Analysis,
 Regression Analysis,
 Quality Control Procedures, 
Review & Analysis of Police Accident Reports
, Forecasting & Time Series
Biomechanics and Automotive Engineering
Statistical/Economic Evaluation
1995
Privately Held
83
9
Research
Porto Alegre, Brazil
The Foundation of Economics and Statistics (FEE) was founded in 1973. It is a research institute of the state government of Rio Grande do Sul, Brazil, whose purpose is to assist economic and social planning. Its main products are studies and statistics analysis concerning the regional economy and society. To carry out its aims FEE counts on an interdisciplinary staff which includes more than 80 senior researchers. Research reports and data sets are presented on the web site (http:/www.fee.rs.gov.br) and may also be found in books, journals and CD-ROM. The main activities undertaken currently by FEE may be broken down into two broad areas: The social and economic analysis area: encompasses studies on agriculture, manufacturing industry, employment and industrial relations, the state and the financial sector, regional and urban studies, international relations, economic history as well as social and political issues. The statistical information area: encompasses updated time series of the Gross State Product of Rio Grande do Sul and municipal product, social indicators and demographic projections.
1973
Public Company
79
10
Utilities
Birkenhead, Wirral
Crowder Consulting provides innovative engineering consultancy services and software solutions to the water industry, specialising in water supply, water distribution and sewer networks. With over 30 years of experience, we provide a range of services to numerous major water service providers, supporting their journey towards Smart Networks. Our core product is Netbase, a Water Network Management System which represents a water operator’s water supply and distribution network, with all associated date series, spatial and time series data. Netbase provides the current operational and monitored status of the water network and supports a wide range of network management, non-revenue water, operational performance and modelling activities. As a multi-user system, Netbase fully supports multiple users on the local network and through remote access. The system is fully spatial and incorporates the ILOG GIS component enabling spatial analysis and maps, but can also be implemented as non-spatial, using the hierarchy of network areas to navigate around the database. In addition to Netbase, we are equipped to provide a wide range of services to the water industry, including hydraulic modelling, leakage detection, asset management and planning, data logging, water efficiency initiatives and various data services.
1985
Privately Held
78

Patents

#NumberTitleAbstractDateKindAssigneeInventor
1
11 042 677
Systems and methods for time series simulation
Systems, apparatuses, methods, and computer program products are disclosed for generating time series. A time series simulator receives information corresponding to a request for time series. The information is formatted into input data by the time series simulator. The input data comprises at least one continuous condition. A generator network of the continuous condition generative adversarial network (CCGAN) generates the time series based directly on a value of the at least one continuous condition. The time series is provided such that the time series is at least one of (a) provided as input to an analysis pipeline or (b) received by a user computing device wherein a representation of at least a portion of the one or more time series is provided via an interactive user interface of the user computing device.
B1
Wells Fargo Bank, N.A.
Shutian Zeng, Jie Chen, Yiping Zhuang, Rao Fu, Agus Sudjianto
2
11 036 715
Combination of techniques to detect anomalies in multi-dimensional time series
Methods, systems, apparatuses, and computer program products are described herein that enable detecting anomalies in time series. An anomaly detection technique is selected from a plurality of detection techniques, and is applied to a first time-series data set (having a first set of dimensions). In response to detecting an anomaly in the time-series data set, the anomaly detection technique is applied to a second time-series data set that is a subset of the first time-series data set. The first time-series data set includes the first set of dimensions plus one or more additional dimensions.
B2
MICROSOFT TECHNOLOGY LICENSING, LLC
Michail Zervos, LeninaDevi Thangavel, Dmitri A. Klementiev, Kateryna Boikovska, Dinko Papak, Igor Sakhnov, Varun Jain, Dhruv Gakkhar
3
11 036 766
Time series analysis using a clustering based symbolic representation
Techniques are described for performing a time series analysis using a clustering based symbolic representation. Implementations employ a clustering based symbolic representation applied to time series data. In some implementations, the time series data is discretized into subsequences with regular time intervals, and symbols encoding the time intervals may be derived by performing clustering algorithms on the subsequences. In the new representation, a time series is transformed into a sequence of categorical values. The symbolic representation is suitable to perform time series classification and forecast with higher accuracy and greater efficiency compared to previously used techniques. Through use of the symbolic representation, a dimension reduction is applied to transform the time sequences to a feature space with lower dimensions. As output of such transformation, a new representation is obtained based on the original time series. This new reduced-dimension representation improves the efficiency of time series data mining and forecasting.
B2
Business Objects Software Ltd.
Ying Wu, Paul Pallath
4
11 023 577
Anomaly detection for time series data having arbitrary seasonality
In various implementations, a method includes receiving a set of time series data that corresponds to a metric. A seasonal pattern is extracted from the set of time series data and the extracted seasonal pattern is filtered from the set of time series data. A predictive model is generated from the filtered set of data. The extracted seasonal pattern is filtered from another set of time series data where the second set of time series data corresponds to the metric. The filtered second set of time series data is compared to the predictive model. An alert is generated to a user for a value within the filtered second set of time series data which falls outside of the predictive model.
B2
ADOBE Inc.
Balaji Vasan Srinivasan, Shiv Kumar Saini, Natwar Modani
5
11 023 350
Technique for incremental and flexible detection and modeling of patterns in time series data
The present disclosure describes a flexible technique to learn patterns in time series data that recur over time. The patterns may be used for simulation, predicting future behavior, or detecting anomalies in a system in which the data is collected. The technique incrementally detects daily, weekly, monthly, and yearly patterns. Each pattern is built over time instead of requiring all the data to be available at the beginning of the analysis. Instead of modeling each pattern explicitly, each pattern is described in the context of a day and formed based on time series data collected over an entire day. An example use of the technique is detecting load patterns in a computer system. A metric of system load such as CPU utilization may be collected periodically over a day. The techniques presented herein capture multiple daily models, each representing a different load pattern.
B2
Oracle International Corporation
Sumathi Gopalakrishnan, Sampanna Shahaji Salunke, Dustin Garvey
6
11 018 960
Accelerated time series analysis in a network
Techniques for accelerated Time series analysis (TSA) in a network are described. Packets from a first network flow at a network element, such as a switch or a router, are trapped using a hardware based TSA engine at the network element. The packets are then reduced into TSA tuples including TSA data points and stored into memory. A software based TSA module performs one or more TSA actions on the stored tuples, where the TSA actions produce analysis results used to determine network performance for the network and network based applications.
B2
Cisco Technology, Inc.
Pradeep K. Munakala, Charles Calvin Byers, Ashish K. Dalela, Xiaoguang Jason Chen
7
11 017 296
Classifying time series image data
The present invention extends to methods, systems, and computer program products for classifying time series image data. Aspects of the invention include encoding motion information from video frames in an eccentricity map. An eccentricity map is essentially a static image that aggregates apparent motion of objects, surfaces, and edges, from a plurality of video frames. In general, eccentricity reflects how different a data point is from the past readings of the same set of variables. Neural networks can be trained to detect and classify actions in videos from eccentricity maps. Eccentricity maps can be provided to a neural network as input. Output from the neural network can indicate if detected motion in a video is or is not classified as an action, such as, for example, a hand gesture.
B2
FORD GLOBAL TECHNOLOGIES, LLC
Dimitar Petrov Filev, Pavithra Madhavan, Gintaras Vincent Puskorius, Gaurav Kumar Singh, Bruno Jales Costa
8
11 018 963
Dynamic granularity of time series data based on network conditions
Technology is described for receiving time series data to be transmitted to a server. A network connectivity problem may be determined to exist for a computer network with the server which prevents the time series data from being transmitted to the server. A downsampling function may be applied to the time series data to produce reduced granularity data points that represent an approximation of the time series data to be transmitted to the server after the network connectivity problem has occurred with the server. The reduced granularity data points may be transmitted to the server.
B1
Amazon Technologies, Inc.
Pascal Hahn, Sergejus Barinovas
9
11 010 689
Machine learning for time series using semantic and time series data
Techniques that facilitate semantic and time series analysis using machine learning are provided. In one example, a system includes a data analysis component, a prediction component and a learning component. The data analysis component that establishes one or more relationships between one or more elements of semantic data, including one or more time series identifiers, and one or more elements of time series data in a relationship database. The prediction component generates one or more advisory outputs, wherein generation of the one or more advisory outputs is performed in response to a trigger event. A learning component that determines the one or more relationships in the relationship database, wherein determination of the one or more relationships is based on information indicative of whether the advisory outputs satisfy a defined criterion.
B2
INTERNATIONAL BUSINESS MACHINES CORPORATION
Bradley Eck, Vincent Lonij, Pascal Pompey
10
11 010 362
Method and system for caching a generated query plan for time series data
In a method for caching a generated query plan for time series data, a query plan for time series data is generated based on a query comprising an expression, the query plan including a path of execution for resolving the query. The path of execution of the query plan to resolve the query is executed. A result of the path of execution is returned as a response to the query. The query plan is cached for retrieval and execution responsive to receiving another query that matches the query, such that the query plan can be used to resolve the another query.
B2
VMware, Inc.
Clement Pang

Patents by Year

Inventors

Assignees

Assignees

Science

Data limited by 2021

Top 10 cited papers

#Paper TitlePaper AbstractAuthorsFields of StudyYearCitation Count
1
Testing for a Unit Root in Time Series Regression
This Paper Proposes Some New Tests for Detecting the Presence of a Unit Root in Quite General Time Series Modesl. Our Approach Is Nonparametric with Respect to Nuisance Parameters and Thereby Allows for a Very Wide Class of Weakly Dependent and Possibly Heterogeneously Distributed Data. the Tests Accomodate Models with a Fitted Drift and a Time Trend So That They May Be Used to Discriminate Between Unit Root Nonstationarity and Stationarity About a Deterministic Trend. the Limiting Distributions of the Statistics Are Obtained Under Both the Unit Root Null and a Sequence of Local Alternatives. Th Latter Noncentral Distribution Theory Yields Local Asymptotic Power Functions for the Tests. These Are Compared with Alternative Procedures Due to Dickey and Fuller. an Exemple Is Provided.
Economics, Mathematics
1986
16 247
2
Time series analysis, forecasting and control
From the Publisher: This is a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application —forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control. Features sections on: recently developed methods for model specification, such as canonical correlation analysis and the use of model selection criteria; results on testing for unit root nonstationarity in ARIMA processes; the state space representation of ARMA models and its use for likelihood estimation and forecasting; score test for model checking; and deterministic components and structural components in time series models and their estimation based on regression-time series model methods.
Economics, Computer Science
1970
12 697
3
Time Series Analysis
We provide a concise overview of time series analysis in the time and frequency domains, with lots of references for further reading.
Computer Science
1986
11 779
4
Time series analysis, forecasting and control
time series analysis san francisco state university, 6 4 introduction to time series analysis, box and jenkins time series analysis forecasting and, th15 weeks citation classic eugene garfield, proc arima references 9 3 sas support, time series analysis forecasting and control pambudi, time series analysis forecasting and control george e, time series analysis forecasting and control ebook, time series analysis forecasting and control 5th edition, time series analysis forecasting and control fourth, time series analysis forecasting and control amazon, wiley time series analysis forecasting and control 5th, time series analysis forecasting and control edition 5, time series analysis forecasting and control 5th edition, time series analysis forecasting and control abebooks, time series analysis for business forecasting, time series analysis forecasting and control wiley, time series analysis forecasting and control book 1976, time series analysis forecasting and control researchgate, time series analysis forecasting and control edition 4, time series analysis forecasting amp control forecasting, george box publications department of statistics, time series analysis forecasting and control london, time series analysis forecasting and control an, time series analysis forecasting and control amazon it, box g e p and jenkins g m 1976 time series, time series analysis forecasting and control pdf slideshare, time series analysis forecasting and control researchgate, time series analysis forecasting and control 5th edition, time series analysis forecasting and control 5th edition, time series wikipedia, time series analysis forecasting and control abebooks, time series analysis forecasting and control, forecasting and time series analysis using the sca system, time series analysis forecasting and control by george e, time series analysis forecasting and control 5th edition, time series analysis forecasting and control 5th edition, box and jenkins time series analysis forecasting and control, time series analysis forecasting and control ebook, time series analysis forecasting and control, time series analysis and forecasting cengage, 6 7 references itl nist gov, time series analysis forecasting and control george e, time series analysis and forecasting statgraphics, time series analysis forecasting and control fourth edition, time series analysis forecasting and control, time series analysis forecasting and control wiley, time series analysis forecasting and control in
Environmental Science, Computer Science, Business
1972
11 664
5
Applied Econometric Time Series
PREFACE. ABOUT THE AUTHOR. Chapter DIFFERENCE EQUATIONS . 1 Time-Series Models. 2 Difference Equations and Their Solutions. 3 Solution by Iteration. 4 An Alternative Solution Methodology. 5 The Cobweb Model. 6 Solving Homogeneous Difference Equations. 7 Finding Particular Solutions for Deterministic Processes. 8 The Method of Undetermined Coefficients. 9 Lag Operators. Summary and Conclusions. Questions and Exercises. Endnotes. Appendix 1 Imaginary Roots and de Moivre's Theorem. Appendix 2 Characteristic Roots in Higher-Order Equations. Chapter 2 STATIONARY TIME-SERIES MODELS . 1 Stochastic Difference Equation Models. 2 ARMA Models. 3 Stationarity. 4 Stationarity Restrictions for an ARMA(p, q) Model. 5 The Autocorrelation Function. 6 The Partial Autocorrelation Function. 7 Sample Autocorrelations of Stationary Series. 8 Box-Jenkins Model Selection. 9 Properties of Forecasts. 10 A Model of the Interest Rate Spread. 11 Seasonality. 12 Parameter Instability and Structural Change. Summary and Conclusions. Questions and Exercises. Endnotes. Appendix 1 Estimation of an MA(1) Process. Appendix 2 Model Selection Criteria. Chapter 3 MODELING VOLATILITY . 1 Economic Time Series The Stylized Facts. 2 ARCH Processes. 3 ARCH and GARCH Estimates of Inflation. 4 Two Examples of GARCH Models. 5 A GARCH Model of Risk. 6 The ARCH-M Model. 7 Additional Properties of GARCH Processes. 8 Maximum Likelihood Estimation of GARCH Models. 9 Other Models of Conditional Variance. 10 Estimating the NYSE International 100 Index. 11 Multivariate GARCH. Summary and Conclusions. Questions and Exercises. Endnotes. Appendix 1 Multivariate GARCH Models. Chapter 4 MODELS WITH TREND . 1 Deterministic and Stochastic Trends. 2 Removing the Trend. 3 Unit Roots and Regression Residuals. 4 The Monte Carlo Method. 5 Dickey-Fuller Tests. 6 Examples of the ADF Test. 7 Extensions of the Dickey-Fuller Test. 8 Structural Change. 9 Power and the Deterministic Regressors. 10 Tests with More Power. 11 Panel Unit Root Tests. 12 Trends and Univariate Decompositions. Summary and Conclusions. Questions and Exercises. Endnotes. Appendix 1 The Bootstrap. Chapter 5 MULTIEQUATION TIME-SERIES MODELS . 1 Intervention Analysis. 2 Transfer Function Models. 3 Estimating a Transfer Function. 4 Limits to Structural Multivariate Estimation. 5 Introduction to VAR Analysis. 6 Estimation and Identification. 7 The Impulse Response Function. 8 Testing Hypothesis. 9 Example of a Simple VAR Terrorism and Tourism in Spain. 10 Structural VARs. 11 Examples of Structural Decompositions. 12 The Blanchard and Quah Decomposition. 13 Decomposing Real and Nominal Exchange Rate Movements An Example. Summary and Conclusions. Questions and Exercises. Endnotes. Chapter 6 COINTEGRATION AND ERROR-CORRECTION MODELS . 1 Linear Combinations of Integrated Variables. 2 Cointegration and Common Trends. 3 Cointegration and Error Correction. 4 Testing for Cointegration The Engle-Granger Methodology. 5 Illustrating the Engle-Granger Methodology. 6 Cointegration and Purchasing-Power Parity. 7 Characteristic Roots, Rank, and Cointegration. 8 Hypothesis Testing. 9 Illustrating the Johansen Methodology. 10 Error-Correction and ADL Tests. 11 Comparing the Three Methods. Summary and Conclusions. Questions and Exercises. Endnotes. Appendix 1 Characteristic Roots Stability and Rank. Appendix 2 Inference on a Cointegrating Vector. Chapter 7 NONLINEAR TIME-SERIES MODELS . 1 Linear Versus Nonlinear Adjustment. 2 Simple Extensions of the ARMA Model. 3 Regime Switching Models. 4 Testing For Nonlinearity. 5 Estimates of Regime Switching Models. 6 Generalized Impulse Responses and Forecasting. 7 Unit Roots and Nonlinearity. Summary and Conclusions. Questions and Exercises. Endnotes. STATISTICAL TABLES. A. Empirical Cumulative Distributions of the tau. B. Empirical Distribution of PHI . C. Critical Values for the Engle-Granger Cointegration Test. D. Residual Based Cointegration Test with I (1) and I (2) Variables. E. Empirical Distributions of the lambda max and lambda trace Statistics. F. Critical Values for beta 1 = 0 in the Error-correction Model. G. Critical Values for Threshold Unit Roots. REFERENCES. SUBJECT INDEX.
Economics, Mathematics
1994
6 441
6
Time Series Analysis: Forecasting and Control
Advances in Time Series Analysis and ForecastingThe Analysis of Time SeriesForecasting: principles and practiceIntroduction to Time Series Analysis and ForecastingThe Oxford Handbook of Quantitative Methods, Vol. 2: Statistical AnalysisTime-Series ForecastingPractical Time Series AnalysisApplied Bayesian Forecasting and Time Series AnalysisSAS for Forecasting Time SeriesApplied Time Series AnalysisTime Series AnalysisElements of Nonlinear Time Series Analysis and ForecastingTime Series Analysis and Forecasting by ExampleIntroduction to Time Series Analysis and ForecastingTime Series Analysis and AdjustmentSpatial Time SeriesPractical Time Series Forecasting with RA Very British AffairMachine Learning for Time Series Forecasting with PythonTime Series with PythonTime Series Analysis: Forecasting & Control, 3/EIntroduction to Time Series Forecasting With PythonThe Analysis of Time SeriesTime Series Analysis and Its ApplicationsForecasting and Time Series AnalysisIntroduction to Time Series and ForecastingIntroduction to Time Series Analysis and ForecastingTime Series Analysis in the Social SciencesPractical Time Series AnalysisTime Series Analysis and ForecastingTheory and Applications of Time Series AnalysisApplied Time SeriesSAS for Forecasting Time Series, Third EditionTime Series AnalysisPredictive Modeling Applications in Actuarial ScienceIntroductory Time Series with RHands-On Time Series Analysis with RAdvances in Time Series ForecastingTime Series Analysis and Forecasting Using Python & RAdvanced Time Series Data Analysis
Environmental Science, Computer Science, Mathematics, Economics
1976
5 705
7
Regression and time series model selection in small samples
SUMMARY A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. The correction is of particular use when the sample size is small, or when the number of fitted parameters is a moderate to large fraction of the sample size. The corrected method, called AICC, is asymptotically efficient if the true model is infinite dimensional. Furthermore, when the true model is of finite dimension, AICC is found to provide better model order choices than any other asymptotically efficient method. Applications to nonstationary autoregressive and mixed autoregressive moving average time series models are also discussed.
Mathematics
1989
5 491
8
Introduction to Statistical Time Series.
Moving Average and Autoregressive Processes. Introduction to Fourier Analysis. Spectral Theory and Filtering. Some Large Sample Theory. Estimation of the Mean and Autocorrelations. The Periodogram, Estimated Spectrum. Parameter Estimation. Regression, Trend, and Seasonality. Unit Root and Explosive Time Series. Bibliography. Index.
Computer Science, Mathematics
1976
5 128
9
Forecasting, Structural Time Series Models and the Kalman Filter
In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.
Economics, Engineering
1990
4 854
10
Introduction to time series and forecasting
Preface 1 INTRODUCTION 1.1 Examples of Time Series 1.2 Objectives of Time Series Analysis 1.3 Some Simple Time Series Models 1.3.3 A General Approach to Time Series Modelling 1.4 Stationary Models and the Autocorrelation Function 1.4.1 The Sample Autocorrelation Function 1.4.2 A Model for the Lake Huron Data 1.5 Estimation and Elimination of Trend and Seasonal Components 1.5.1 Estimation and Elimination of Trend in the Absence of Seasonality 1.5.2 Estimation and Elimination of Both Trend and Seasonality 1.6 Testing the Estimated Noise Sequence 1.7 Problems 2 STATIONARY PROCESSES 2.1 Basic Properties 2.2 Linear Processes 2.3 Introduction to ARMA Processes 2.4 Properties of the Sample Mean and Autocorrelation Function 2.4.2 Estimation of $\gamma(\cdot)$ and $\rho(\cdot)$ 2.5 Forecasting Stationary Time Series 2.5.3 Prediction of a Stationary Process in Terms of Infinitely Many Past Values 2.6 The Wold Decomposition 1.7 Problems 3 ARMA MODELS 3.1 ARMA($p,q$) Processes 3.2 The ACF and PACF of an ARMA$(p,q)$ Process 3.2.1 Calculation of the ACVF 3.2.2 The Autocorrelation Function 3.2.3 The Partial Autocorrelation Function 3.3 Forecasting ARMA Processes 1.7 Problems 4 SPECTRAL ANALYSIS 4.1 Spectral Densities 4.2 The Periodogram 4.3 Time-Invariant Linear Filters 4.4 The Spectral Density of an ARMA Process 1.7 Problems 5 MODELLING AND PREDICTION WITH ARMA PROCESSES 5.1 Preliminary Estimation 5.1.1 Yule-Walker Estimation 5.1.3 The Innovations Algorithm 5.1.4 The Hannan-Rissanen Algorithm 5.2 Maximum Likelihood Estimation 5.3 Diagnostic Checking 5.3.1 The Graph of $\t=1,\ldots,n\ 5.3.2 The Sample ACF of the Residuals
Computer Science, Mathematics
1996
4 324

Top 10 cited authors

#AuthorPapers countCitation Count
1
14
42 100
2
39
38 387
3
31
37 285
4
43
33 593
5
183
29 027
6
20
28 600
7
5
19 999
8
13
18 083
9
3
17 935
10
1
16 789

Science papers by Year

Clinical Trials

  • Researches Count 20
  • Ongoing Studies 6
  • Total Enrollment 8 816 990

Clinical Trials by Year

Countries

Phases

Clinical Trials

#TitleConditionsInterventionsEnrollmentYearLocations
1
A Quasi-experimental, Interrupted Time Series Study to Evaluate the Effectiveness of "SexHealth Mobile" on Uptake of Contraception in Women With Substance Use Disorder
Contraception, Contraceptive Usage
SexHealth Mobile
170
2020
Children's Mercy Hospital Kansas City
2
Transforming Hypertension Treatment in Nigeria Using a Type II Hybrid, Interrupted Time Series Design
Hypertension
Evidence-based hypertension treatment
57 600
2020
Northwestern University
3
The Effect of Calcium-based and Calcium-free Phosphate-binders on Bone Mineral Content, Measured by a Novel Technique of Dual-tracer Stable Calcium Isotope Method, in Children With Chronic Kidney Disease or on Dialysis - a Time Series Trial
Chronic Kidney Diseases
calcium carbonate, sevelamer carbonate
21
2020
University College, London
4
The Effect of a Specialist Paramedic Primary Care Rotation on Appropriate Non-conveyance Decision: a Controlled Interrupted Time Series Analysis
Safe Paramedic Non-conveyance
10-week rotation in primary care setting
33 600
2020
Yorkshire Ambulance Service NHS Trust
5
Time Series Model For Forecasting the Number of Covid-19 Cases Worldwide - A Prospective Cohort Study
Covid-19
Model Building, Model validation
7 882 471
2019
Turkish Ministry of Health Izmir Teaching Hospital
6
Antibiotic Stewardship Program in Pancreatic Surgery: a Multicenter Time Series Analysis (BIOSTEPS).
Antibiotic Resistant Infection, Surgical Site Infection
Antibiotic Stewardship Program
1 200
2019
Azienda Ospedaliera Universitaria Integrata Verona
7
Impact of Short-term Air Pollution Exposure on Acute Coronary Syndrome in Two Cohorts of Industrial and Non-industrial Areas: A Time Series Regression With 6,000,000 Person-years of Follow-up (ACS - Air Pollution Study)
Acute Coronary Syndrome, Air Pollution
Acute coronary syndrome
9 046
2019
Medical University of Silesia
8
Impact of Familial Cancer Specialist Recommendation on Chemoprevention Prescribing for Familial Breast Cancer (FBC) Risk in Primary Care: Short Survey and Interrupted Time Series Analysis
Familial Breast Cancer
Survey Administration
100
2019
University of Nottingham
9
Effects of Multi-Disciplinary Training Therapy On Binge Eating Episodes
Binge-Eating Disorder, Group Meetings, Obesity, Weight Loss
Multi-disciplinary obesity management therapy
118
2019
Adana City Training and Research Hospital
10
Single-Arm, Non-Randomized, Time Series, Single-Subject Study: Fecal Microbiota Transplantation (FMT) in Multiple Sclerosis
Multiple Sclerosis, Relapsing-Remitting
Fecal Microbiota Transplantation (FMT)
1
2018
Rush University Medical Center

Trends

#LinkTrendsRank
1
158
2
158
3
41 355

Use Cases

#TopicPaper TitleYearFields of studyCitationsUse CaseAuthors
1
Time Series
A 30-year monthly 5 km gridded surface elevation time series for the Greenland Ice Sheet from multiple satellite radar altimeters
2022
1
the greenland ice sheet from multiple satellite radar altimeters
2
Time Series
A Generative Deep Learning Framework Across Time Series to Optimize the Energy Consumption of Air Conditioning Systems
2022
Computer Science
0
optimize the energy consumption of air conditioning systems
3
Time Series
A New Earth Observation Service Based on Sentinel-1 and Sentinel-2 Time Series for the Monitoring of Redevelopment Sites in Wallonia, Belgium
2022
0
the monitoring of redevelopment sites in wallonia, belgium
4
Time Series
A Novel Adjusted Land Surface Temperature for the Prediction of Water Bodies (Alst-W) Using Time Series Machine Learning Model
2022
0
a novel adjusted land surface temperature for the prediction of water bodies (alst-w)
5
Time Series
A Novel Approach for Analytical Comparison of Cryptocurrencies using Time Series
2022
0
a novel approach for analytical comparison of cryptocurrencies
6
Time Series
A Novel Method for IPTV Customer Behavior Analysis Using Time Series
2022
Computer Science
0
a novel method for iptv customer behavior analysis
7
Time Series
Adaptive Processing of Technological Time Series for Forecasting Based on Neuro-Fuzzy Networks
2022
0
forecasting based on neuro-fuzzy networks
8
Time Series
Aggregated Traffic Anomaly Detection Using Time Series Forecasting on Call Detail Records
2022
0
aggregated traffic anomaly detection
9
Time Series
An Effective Ensemble Framework with Multichannel Time Series for User Retention Prediction
2022
0
user retention prediction
10
Time Series
Análise do volume do reservatório da usina hidrelétrica de Três Marias usando modelos de séries temporais / Analysis of reservoir volume at the Três Marias hydroelectric plant using time series models
2022
0
análise do volume do reservatório da usina hidrelétrica de três marias usando modelos de séries temporais / analysis of reservoir volume at the três marias hydroelectric plant

Case Studies

#TitleDescriptionPDFYearSource Ranking
1
(PDF) Online detection of offsets in GPS time series: A case ...
Feb 8, 2022 — https://doi.org/10.1007/s12145-020-00517-x. RESEARCH ARTICLE. Online detection of offsets in GPS time series. A case study of eruptive ...
no
2022
6 650
2
A Case Study of NDVI Time Series Analysis in Chongqing - IT ...
Mar 1, 2022 — Fonctions:. ui.Chart.image.doySeries(imageCollection, region, regionReducer, scale, yearReducer, startDay, endDay).
no
2022
0
3
a case study of NDVI time series analysis line chart ... - 文章整合
Mar 1, 2022 — How does the clikhouse database sort by the first letter of a string type field · Property 'sqlSessionFactory' or 'sqlSessionTemplate' are ...
no
2022
10
4
A Case Study of Time Series Analysis to Forecast ... - Digiwire
Feb 16, 2022 — The number of tuberculosis (TB) cases in Khartoum state was predicted using time series analysis. It is based on data collected from ...
no
2022
0
5
A Case Study of Time Series Analysis to Forecast the Number ...
by AEAA Mohammed · 2022 — This paper used time series analysis to predict the number of tuberculosis (TB) patients in Khartoum state. It is based on data obtained from TB ...
no
2022
90
6
Generating high-quality synthetic time-series data for a top 5 ...
Jan 17, 2022 — Case Study: Generating high-quality synthetic time-series data for a top 5 global bank · machinelearning · datascience · privacy · showdev.
no
2022
820
7
Time Series Analysis Model for Rainfall Data in Jordan
Feb 25, 2022 — Download Citation | Time Series Analysis Model for Rainfall Data in Jordan: Case Study for Using Time Series Analysis | Problem statement: ...
no
2022
6 650
8
ACM Queue Case Study Q&A: Always-On Time-Series ... - On24
Jan 12, 2021 — ... organization that performs telemetry analysis on an already large and exponentially growing number of IoT (Internet of Things) devices.
no
2021
360
9
Adding external factors in time series forecasting. Case study
Feb 2, 2021 — Boström, Henrik. Tipo de Documento: Tesis (Master). Título del máster: Data Science. Fecha: 2020. Materias ...
no
2021
270
10
Analysis of modified time series prediction techniques
by R Sharma · 2021 — E-mail:[email protected] Abstract. In the current scenario, it is a very difficult task to predict the demand for a particular product in the market.
yes
2021
990

Experts

Twitter

#NameDescriptionFollowersFollowingLocation
1
PrometheusMonitoring
An open-source service monitoring system and time series database.
42 794
90
-
2
Turner Motorsport
Turner Motorsport | Racing with BMW | Seven Time Series Champions | GT3 and GT4 Competitors in @IMSA, @GTWorldChAm and @iRacing
14 662
201
Amesbury, MA
3
FTSO.UK
Flare Time Series Oracle on the @FlareNetworks | U.K Based, Global coverage | Providing robust, Weighted signals | Unlocking Value | Delegate your vote and earn
11 253
136
U.K
4
profitx
AI Powered Athledex | Front Office Optics | 17 Real-Time Contract Value + Time Series Models | Contract & Performance Projections |
4 595
17
New York, NY
5
UseYourSpark
UK Signal Provider for the Flare Time Series Oracle #FTSO. Delegate your $SGB #Songbird & $FLR #Spark votes to earn risk-free rewards and compound your wealth.
3 709
244
-
6
Mathias Herberts
Disruptive Engineer, Chief Technology Officer, Co-Founder of @SenXHQ, maker of @Warp10io - Obsessed with Time Series
3 476
3 741
-
7
Tamara
Author of the Shadowstar Legacy and A Paradox of Time series. #scifi & #digitalart Graphic Designer and Owner of Sapphire X Designs. Freelancer with Chaosium.
2 721
3 628
Ireland
8
L. M. Montes | The Triunix of Time
Writer of #urbanfantasy. Lover of coffee and popcorn. Working on book 2 in the Time Series. Author of The Triunix of Time (book 1 in the Time Series).
2 694
2 247
American Northwest
9
Juan D’Amico
Economist - Master’s in Business Economics at Wilfrid Laurier University, Canada. Interested in Macroeconomics and Time Series research topics. 🇨🇦🇦🇷
2 130
2 443
Waterloo, Ontario
10
Patrick Kidger
Maths+ML PhD student at Oxford. Neural ODEs+SDEs+CDEs, time series, rough analysis. (Also ice skating, martial arts and scuba diving!)
1 702
64
University of Oxford

Quora Profiles

#NameAnswersFollowersLocationViewsTopicTopic LinkAnswers to topic
1
93
59
New Delhi
167 271
Time Series Analysis
93
2
160
24
132 802
Time Series
160
3
14
53
London
81 206
Time Series
14
4
11
55
Chicago (city)
78 431
Time Series
11
5
9
24
50 097
Time Series Analysis
9
6
10
39
Jette
39 557
Time Series
10
7
24
506
Bengaluru, Karnataka, India
22 555
Time Series
24
8
4
2
9 499
Time Series
4
9
17
113
Kolkata, West Bengal, India
8 631
Time Series
17
10
2
0
6 298
Time Series Analysis
2

Youtube Channels

#NameDescriptionReg DateViewsCountry
1
Hi! I'm Colt. As you can see, my channel has drastically changed from my initial content. If you're wondering how my vlogs and reaction videos turned into J-Hope content, well, he has affected my life for the better, so I've decided to dedicate my channel to him. I want to highlight my bias as there are not enough videos on the internet about this beautiful man. I want to remind everyone about J-Hope's talent, personality, and more. I also want to share these with those who are new to the fandom. Unlike other fan-made videos, where they just gather random clips and put them into one, mine has a story. My videos can make you laugh, cry, be inspired, and a lot more. I always add my personality to each of my videos by inserting text commentaries and memes in between clips. Aside from these videos, I also have a Story Time series. And since I formed a great friendship with my subscribers, who I call the Sunshine Squad, I do live streaming once in a while to connect with them.
Wed, 13 Jul 2016
12 281 386
Philippines
2
Videos about the racing game Real Racing 3 RR3 Including videos about: - Real Racing 3 team challenges - Real Racing 3 new cars - Real Racing 3 free cars - Real Racing 3 limited time series info - Real Racing 3 special events - Real Racing 3 flashback - Real Racing 3 walkthroughs - Real Racing 3 updates - Real Racing 3 bugs (no cheat) - Real Racing 3 gameplay - Real Racing 3 tips and more..!!!! Please subscribe and share if you like my videos. Devices i play on: Iphone 11 & Ipad 2019 If you have any clips of crazy and beautyfull drifts, powerslides or huge crashes send them to my email 👇 and maybe you can see your clip back in one of my videos
Sat, 1 Dec 2012
9 019 864
United States
3
I am the insightful lumberjack and with my videos I try to provide the world with some insightful lumber. The content ranges from comedy sketches, to exciting adventures, to movie watching advice. On the channel, you can find a couple series. First, there's the "How To Have A Good Time" Series in which I take my camera along on my travels and put together a few steps for anyone to follow and have an amazing time on a specific adventure. Next, I have "Top 5" where I review a specific genre/group of movies hoping to give the world a more diverse selection of movies to watch. Last, but not least, I have series of songs I have written for my family members birthdays. Every song is co performed with my immensely talented friend Isabella, and there are many songs to come because I have a ginormous family and everyone in it means the world to me. Overall, the channel can be describe as random goofiness. I post these videos to have fun and make people laugh. Hopefully you will enjoy!
Fri, 15 May 2015
9 004 861
4
You can add &fmt=18 at the end of the url of the video, and then you can download it in high quality. 2008-2009 Kevin Garnett Game Highlights http://www.youtube.com/view_play_list?p=033A004F933400F0 KG Press Conference http://www.youtube.com/view_play_list?p=18A5347DFB98CE1D Celtics Vintage & Rare Videos http://www.youtube.com/view_play_list?p=7A00FB766ED5269D Celtics Fastbreak Series http://www.youtube.com/view_play_list?p=5264587F3C3C07C1 Celtics Pregame Promotion http://www.youtube.com/view_play_list?p=B059895894007D12 Celtics Action Series http://www.youtube.com/view_play_list?p=9384EEE52BF2ABB1 Celtics It's Game Time Series http://www.youtube.com/view_play_list?p=4495C54BA858F4E1 Celtics VS Raptors Game In Six Series http://www.youtube.com/view_play_list?p=058B1268AECE8FF2 KG's Teammates & Predecessor http://www.youtube.com/view_play_list?p=30A9765C38CD016A
Sun, 23 Jul 2006
7 084 609
5
G'day guys, Welcome to my channel! My name is Shane :) I am better known for my 99 one at a time series on RuneScape, Although I have some other videos too!! Check em out and let me know what you think!
Sat, 30 Aug 2008
3 357 166
Australia
6
This channel mostly show photography of trains and railroads around the San Joaquin Valley, including BNSF, Union Pacific, G&W(SJVR), and any other roads that may pass through. Train videos are not the only thing you will find here, look around train simulators you will find, as well as a part time series Euro Truck Simulator 2! So click the subscribe button, and watch some videos!
Sun, 21 Mar 2010
3 038 045
7
Welcome to my channel! I'm a huge fan of the Wheel of Time series written by Robert Jordan and finished by Brandon Sanderson! This channel will be dedicated to both the book series, merchandise and the upcoming Amazon Prime show. It will be a place where I can share my passion for this series as well as interact with others in the WoT community! Follow me on Twitter @WoTUp5 Contact info if you have any show information to pass along [email protected]
Fri, 11 Oct 2019
1 781 829
Canada
8
Hello Reader, This channel is specially dedicated to The Land Before Time Series. In this channel you will find videos of The Land Before Time including short clips and trailers from all the movies (including different versions). Unfortunately, due to Universal and the copyright rules, I am not allowed to post clips elongating 2 minutes, so please don't ask for that or full-length movies. Anyway, I hope you enjoy your stay, and again... WELCOME TO THE LAND BEFORE TIME.
Sun, 5 Sep 2010
1 041 877
United States
9
Time Series Analysis, Auto-Correlation Function (ACF) and Partial Auto-Correlation Function (PACF), Seasonality Analysis, GARCH Model in Stock Price Forecasting Tutorials. Data science and business analytics in R, Python, and Excel tutorials. Applied business analytics: U.S. housing market crash forecast and analysis. Applied personal finance & investing: Cryptocurrencies and on sale product reviews.
Fri, 7 Mar 2014
912 253
United States
10
I have worked as a Senior Quant for Deutsche Bank and studied at London School of Economics, Oxford University and Cambridge University. The purpose of this channel is to widely disseminate the knowledge of tools and techniques used in Mathematical/Quantitative Finance and Quantitative Business Analytics. I also do consulting and onsite training for R, Probability, Time Series and Monte Carlo simulation. I am based in Carlisle, Pennsylvania and District of Columbia. Contact (training/partnership) :: [email protected] or leave me a message on this channel.-- Harpreet Bedi
Fri, 17 Jan 2014
696 295