相关论文
2000
Time Series Models in Non‐Normal Situations: Symmetric Innovations

We consider AR(q) models in time series with non‐normal innovations represented by a member of a wide family of symmetric distributions (Student's t). Since the ML (maximum likelihood) estimators are intractable, we derive the MML (modified maximum likelihood) estimators of the parameters and show that they are remarkably efficient. We use these estimators for hypothesis testing, and show that the resulting tests are robust and powerful.

2010 - Expert Syst. Appl.
TAIFEX and KOSPI 200 forecasting based on two-factors high-order fuzzy time series and particle swarm optimization

Since the fuzzy time series forecasting methods provide a powerful framework to cope with vague or ambiguous problems, they have been widely used in real applications. The forecasting accuracy of these methods usually, however, depend on their universe of discourse and the length of intervals. So, we present a new forecasting method using two-factors high-order fuzzy time series and particle swarm optimization (PSO) for increasing the forecasting accuracy. To show the effectiveness of the proposed method, we applied our method for the Taiwan futures exchange (TAIFEX) forecasting and the Korea composite price index (KOSPI) 200 forecasting. The results show better forecasting accuracy than previous methods.

2019 - Future Gener. Comput. Syst.
LSTM-EFG for wind power forecasting based on sequential correlation features

Abstract Amid the gradual increase of wind power generation, how to relieve the pressure of peak load and frequency regulation to the power system by wind power forecasting to make it run steadily becomes a key issue. Due to the continuous development of the field of artificial intelligence, neural network, as a machine learning technology, has shown a good predictive effect in time series data forecasting. Long-term short-term Memory is a kind of time recursive neural network, which is suitable for processing and predicting events with relatively Long intervals and delays in time series. This paper proposes an improved Long Short-Term Memory-enhanced forget-gate network model, abbreviated as LSTM-EFG, used to forecasting wind power. Based on the correlation, the features data of turbine groups in certain distance are filtered to further optimize the forecasting effect on wind power by clustering. The results show that the method with Spectral Clustering has an higher accuracy with an increase of 18.3% than those of the other forecasting models, and at the same time the convergence process has been sped up.

2012 - Journal of Business Economics and Management
A comparative analysis of alternative univariate time series models in forecasting Turkish inflation

This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at earlier forecast horizons conventional models, especially ARFIMA and ARIMA, provide better one-step ahead forecasting performance. However, unobserved components model turns out to be the best performer in terms of dynamic forecasts. The superiority of the unobserved components model suggests that inflation in Turkey has time varying pattern and conventional models are not able to track underlying trend of inflation in the long run.

2001 - Physical Review Letters
Surrogate Test for Pseudoperiodic Time Series Data

For time series exhibiting strong periodicities, standard (linear) surrogate methods are not useful. We describe a new algorithm that can test against the null hypothesis of a periodic orbit with uncorrelated noise. We demonstrate the application of this method to artificial data and experimental time series, including human electrocardiogram recordings during sinus rhythm and ventricular tachycardia.

2009 - CHI
Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations

We investigate techniques for visualizing time series data and evaluate their effect in value comparison tasks. We compare line charts with horizon graphs - a space-efficient time series visualization technique - across a range of chart sizes, measuring the speed and accuracy of subjects' estimates of value differences between charts. We identify transition points at which reducing the chart height results in significantly differing drops in estimation accuracy across the compared chart types, and we find optimal positions in the speed-accuracy tradeoff curve at which viewers performed quickly without attendant drops in accuracy. Based on these results, we propose approaches for increasing data density that optimize graphical perception.

1995
The simulation smoother for time series models

SUMMARY Recently suggested procedures for simulating from the posterior density of states given a Gaussian state space time series are refined and extended. We introduce and study the simulation smoother, which draws from the multivariate posterior distribution of the disturbances of the model, so avoiding the degeneracies inherent in state samplers. The technique is important in Gibbs sampling with non-Gaussian time series models, and for performing Bayesian analysis of Gaussian time series.

2006 - Journal of Accounting Research
Comparing the Post–Earnings Announcement Drift for Surprises Calculated from Analyst and Time Series Forecasts

Post-earnings announcement drift is the tendency for a stock's cumulative abnormal returns to drift in the direction of an earnings surprise for several weeks following an earnings announcement. We show that the drift is significantly larger when defining the earnings surprise using analysts' forecasts and actual earnings from I/B/E/S than when using a time series model based on Compustat earnings data. Neither Compustat's policy of restating earnings nor the inclusion of "special items" in reported earnings contribute significantly to the disparity in drift magnitudes. Rather, our results suggest that this disparity is attributable to differences between analyst forecasts and those of time-series models-or at least to factors correlated with these differences. Further, we document that analyst forecasts lead to return patterns around future earnings announcements that differ from those observed when using time-series models, suggesting that the two types of surprises may capture somewhat different forms of mispricing. Copyright 2006 The Institute of Professional Accounting, University of Chicago.

2004 - IEEE Transactions on Signal Processing
Learning graphical models for stationary time series

Probabilistic graphical models can be extended to time series by considering probabilistic dependencies between entire time series. For stationary Gaussian time series, the graphical model semantics can be expressed naturally in the frequency domain, leading to interesting families of structured time series models that are complementary to families defined in the time domain. In this paper, we present an algorithm to learn the structure from data for directed graphical models for stationary Gaussian time series. We describe an algorithm for efficient forecasting for stationary Gaussian time series whose spectral densities factorize in a graphical model. We also explore the relationships between graphical model structure and sparsity, comparing and contrasting the notions of sparsity in the time domain and the frequency domain. Finally, we show how to make use of Mercer kernels in this setting, allowing our ideas to be extended to nonlinear models.

2007 - ISPA Workshops
ECG Anomaly Detection via Time Series Analysis

Recently, wireless sensor networks have been proposed for assisted living and residential monitoring. In such networks, physiological sensors are used to monitor vital signs e.g. heartbeats, pulse rates, oxygen saturation of senior citizens. Sensor data is sent periodically via wireless links to a personal computer that analyzes the data. In this paper, we propose an anomaly detection scheme based on time series analysis that will allow the computer to determine whether a stream of real-time sensor data contains any abnormal heartbeats. If anomaly exists, that time series segment will be transmitted via the network to a physician so that he/she can further diagnose the problem and take appropriate actions. When tested against the heartbeat data readings stored at the MIT database, our ECG anomaly scheme is shown to have better performance than another scheme that has been recently proposed. Our scheme enjoys an accuracy rate that varies from 70-90% while the other scheme has an accuracy that varies from 40-70%.

2011 - Journal of evaluation in clinical practice
Applying a propensity score-based weighting model to interrupted time series data: improving causal inference in programme evaluation.

Often, when conducting programme evaluations or studying the effects of policy changes, researchers may only have access to aggregated time series data, presented as observations spanning both the pre- and post-intervention periods. The most basic analytic model using these data requires only a single group and models the intervention effect using repeated measurements of the dependent variable. This model controls for regression to the mean and is likely to detect a treatment effect if it is sufficiently large. However, many potential sources of bias still remain. Adding one or more control groups to this model could strengthen causal inference if the groups are comparable on pre-intervention covariates and level and trend of the dependent variable. If this condition is not met, the validity of the study findings could be called into question. In this paper we describe a propensity score-based weighted regression model, which overcomes these limitations by weighting the control groups to represent the average outcome that the treatment group would have exhibited in the absence of the intervention. We illustrate this technique studying cigarette sales in California before and after the passage of Proposition 99 in California in 1989. While our results were similar to those of the Synthetic Control method, the weighting approach has the advantage of being technically less complicated, rooted in regression techniques familiar to most researchers, easy to implement using any basic statistical software, may accommodate any number of treatment units, and allows for greater flexibility in the choice of treatment effect estimators.

2012 - The BMJ
Effect of editors’ implementation of CONSORT guidelines on the reporting of abstracts in high impact medical journals: interrupted time series analysis

Objective To investigate the effect of the CONSORT for Abstracts guidelines, and different editorial policies used by five leading general medical journals to implement the guidelines, on the reporting quality of abstracts of randomised trials. Design Interrupted time series analysis. Sample We randomly selected up to 60 primary reports of randomised trials per journal per year from five high impact, general medical journals in 2006-09, if indexed in PubMed with an electronic abstract. We excluded reports that did not include an electronic abstract, and any secondary trial publications or economic analyses. We classified journals in three categories: those not mentioning the guidelines in their instructions to authors (JAMA and New England Journal of Medicine), those referring to the guidelines in their instructions to authors but with no specific policy to implement them (BMJ), and those referring to the guidelines in their instructions to authors with an active policy to implement them (Annals of Internal Medicine and Lancet). Two authors extracted data independently using the CONSORT for Abstracts checklist. Main outcome Mean number of CONSORT items reported in selected abstracts, among nine items reported in fewer than 50% of the abstracts published across the five journals in 2006. Results We assessed 955 reports of abstracts of randomised trials. Journals with an active policy to enforce the guidelines showed an immediate increase in the level of mean number of items reported (increase of 1.50 items; P=0.0037). At 23 months after publication of the guidelines, the mean number of items reported per abstract for the primary outcome was 5.41 of nine items, a 53% increase compared with the expected level estimated on the basis of pre-intervention trends. The change in level or trend did not increase in journals with no policy to enforce the guidelines (BMJ, JAMA, and New England Journal of Medicine). Conclusion Active implementation of the CONSORT for Abstracts guidelines by journals can lead to improvements in the reporting of abstracts of randomised trials.

2003 - The Journal of applied psychology
A lengthy look at the daily grind: time series analysis of events, mood, stress, and satisfaction.

The present study investigated processes by which job stress and satisfaction unfold over time by examining the relations between daily stressful events, mood, and these variables. Using a Web-based daily survey of stressor events, perceived strain, mood, and job satisfaction completed by 14 university workers, 1,060 occasions of data were collected. Transfer function analysis, a multivariate version of time series analysis, was used to examine the data for relationships among the measured variables after factoring out the contaminating influences of serial dependency. Results revealed a contrast effect in which a stressful event associated positively with higher strain on the same day and associated negatively with strain on the following day. Perceived strain increased over the course of a semester for a majority of participants, suggesting that effects of stress build over time. Finally, the data were consistent with the notion that job satisfaction is a distal outcome that is mediated by perceived strain.

2009 - Encyclopedia of Database Systems
Time Series Data Mining

Data Mining or Knowledge Discovery in Databases (KDD) is an important area of computer sciences. The relevance of this area is due to the enormous quantity of information daily produced by different sources, for instance the web, biological processes, finance, the aeronautic industry, retail, and telecommunications data. A considerable amount of this information represents temporal events which are typically stored in the form of time series. There are several phenomena expected to be identified among databases of this type, namely through motif (pattern) discovery, classification, clustering, query by content, abnormality detection, and forecast of property values. We focus particularly on the area of time series motif discovery (Lin and Keogh 2002) , also known as the extraction of recurrent patterns. These patterns are relevant because they summarise the time series of a domain and help the domain expert understand the database at hand (Ferreira et al. 2006). Figure 1 shows one example of such type of pattern in the context of electroencephalogram (EEG) time series. This specific motif is detected in three different time series in the database.

1998 - The European journal of neuroscience
Seizure prediction by non‐linear time series analysis of brain electrical activity

Brain electrical activity of 16 patients with temporal lobe epilepsy, recorded intracranially during seizure‐free intervals as well as during transitions to the seizure state, was analysed using methods derived from the theory of non‐linear dynamics. Long‐lasting and marked changes towards low‐dimensional system states were found to occur specifically up to 25 min prior to epileptic seizures and allow to predict the occurrence of individual seizures in time. These findings reflect a continuous increase in the degree of synchronicity, and thus open a window for the study of mechanisms generating seizures in humans. This offers new possibilities for therapeutic interventions.

2008 - Econometrics: Econometric & Statistical Methods - Special Topics eJournal
Temporal Aggregation of Univariate and Multivariate Time Series Models: A Survey

We present a unified and up-to-date overview of temporal aggregation techniques for univariate and multivariate time series models explaining in detail, although intuitively, the technical machinery behind the results. Some empirical applications illustrate the main issues.

1988
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.

2001 - Science
Noisy Clockwork: Time Series Analysis of Population Fluctuations in Animals

Both biotic interactions and abiotic random forcing are crucial influences on population dynamics. This frequently leads to roughly equal importance of deterministic and stochastic forces. The resulting tension between noise and determinism makes ecological dynamics unique, with conceptual and methodological challenges distinctive from those in other dynamical systems. The theory for stochastic, nonlinear ecological dynamics has been developed alongside methods to test models. A range of dynamical components has been considered—density dependence, environmental and demographic stochasticity, and climatic forcing—as well as their often complex interactions. We discuss recent advances in understanding ecological dynamics and testing theory using long-term data and review how dynamical forces interact to generate some central field and laboratory time series.

2006 - Signal Process.
Symbolic time series analysis via wavelet-based partitioning

Symbolic time series analysis (STSA) of complex systems for anomaly detection has been recently introduced in literature. An important feature of the STSA method is extraction of relevant information, imbedded in the measured time series data, to generate symbol sequences. This paper presents a wavelet-based partitioning approach for symbol generation, instead of the currently practiced method of phase-space partitioning. Various aspects of the proposed technique, such as wavelet selection, noise mitigation, and robustness to spurious disturbances, are discussed. The waveletbased partitioning in STSA is experimentally validated on laboratory apparatuses for anomaly/damage detection. Its efficacy is investigated by comparison with phase-space partitioning. r 2006 Elsevier B.V. All rights reserved.

2002 - Reviews of Geophysics
ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES

[1] The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory. In this review we describe the connections between time series analysis and nonlinear dynamics, discuss signal-to-noise enhancement, and present some of the novel methods for spectral analysis. The various steps, as well as the advantages and disadvantages of these methods, are illustrated by their application to an important climatic time series, the Southern Oscillation Index. This index captures major features of interannual climate variability and is used extensively in its prediction. Regional and global sea surface temperature data sets are used to illustrate multivariate spectral methods. Open questions and further prospects conclude the review.

Introduction to Time Series Analysis and Forecasting

Murat Kulahci
|
Douglas C. Montgomery
|
Cheryl L. Jennings
|
D. Montgomery
|
M. Kulahci
|
T. Utlaut

Abstract:1. Introduction to Forecasting. 1.1 The Nature and uses of Forecasts. 1.2 Some Examples of Time Series. 1.3 The Forecasting Process. 1.4 Resources for Forecasting. 2. Statistics Background for Forecasting. 2.1 Introduction. 2.2 Graphical Displays. 2.3 Numerical Description of Time Series Data. 2.4 Use of Data Transformations and Adjustments. 2.5 General Approach to Time Series Analysis and Forecasting. 2.6 Evaluating and Monitoring Forecasting Model Performance. 3. Regression Analysis and Forecasting. 3.1 Introduction. 3.2 Least Squares Estimation in Linear Regression Models. 3.3 Statistical Inference in Linear Regression. 3.4 Prediction of New Observations. 3.5 Model Adequacy Checking. 3.6 Variable Selection Methods in Regression. 3.7 Generalized and Weighted Least Squares. 3.8 Regression Models for General Time Series Data. 4. Exponential Smoothing Methods. 4.1 Introduction. 4.2 First-Order Exponential Smoothing. 4.3 Modeling Time series Data. 4.4 Second-Order Exponential Smoothing. 4.5 Higher-Order Exponential Smoothing. 4.6 Forecasting. 4.7 Exponential Smoothing for Seasonal Data. 4.8 Exponential Smoothers and ARIMA Models. 5. Autoregressive Integrated Moving Average (ARIMA) Models. 5.1 Introduction. 5.2 Linear Models for Stationary Time Series. 5.3 Finite Order Moving Average (MA) Processes. 5.4 Finite Order Autoregressive Processes. 5.5 Mixed Autoregressive-Moving Average (ARMA) Processes. 5.6 Non-stationary Processes. 5.7 Time Series Model Building . 5.8 Forecasting ARIMA Processes . 5.9 Seasonal Processes. 5.10 Final Comments. 6. Transfer Function and Intervention Models. 6.1 Introduction. 6.2 Transfer Function Models. 6.3 Transfer Function-Noise Models. 6.4 Cross Correlation Function. 6.5 Model Specification. 6.6 Forecasting with Transfer Function-Noise Models. 6.7 Intervention Analysis. 7. Survey of Other Forecasting Methods. 7.1 Multivariate Time Series Models and Forecasting. 7.2 State Space Models. 7.3 ARCH and GARCH Models. 7.4 Direct Forecasting of Percentiles. 7.5 Combining Forecasts to Improve Prediction Performance. 7.6 Aggregation and Disaggregation of Forecasts. 7.7 Neural Networks and Forecasting. 7.8 Some Comments on Practical Implementation and use of Statistical Forecasting Techniques. Bibliography. Appendix. Appendix A Statistical Tables. Table A.1 Cumulative Normal Distribution. Table A.2 Percentage Points of the Chi-Square Distribution. Table A.3 Percentage Points of the t Distribution. Table A.4 Percentage Points of the F Distribution. Table A.5 Critical Values of the Durbin-Watson Statistic. Appendix B Data Sets for Exercises. Table B.1 Market Yield on U.S. Treasury Securities at 10-year Constant Maturity. Table B.2 Pharmaceutical Product Sales. Table B.3 Chemical Process Viscosity. Table B.4 U.S Production of Blue and Gorgonzola Cheeses. Table B.5 U.S. Beverage Manufacturer Product Shipments, Unadjusted. Table B.6 Global Mean Surface Air Temperature Anomaly and Global CO22 Concentration. Table B.7 Whole Foods Market Stock Price, Daily Closing Adjusted for Splits. Table B.8 Unemployment Rate - Full-Time Labor Force, Not Seasonally Adjusted. Table B.9 International Sunspot Numbers. Table B.10 United Kingdom Airline Miles Flown. Table B.11 Champagne Sales. Table B.12 Chemical Process Yield, with Operating Temperature (Uncontrolled). Table B.13 U.S. Production of Ice Cream and Frozen Yogurt. Table B.14 Atmospheric CO2 Concentrations at Mauna Loa Observatory. Table B.15 U.S. National Violent Crime Rate. Table B.16 U.S. Gross Domestic Product. Table B.17 U.S. Total Energy Consumption. Table B.18 U.S. Coal Production. Table B.19 Arizona Drowning Rate, Children 1-4 Years Old. Table B.20 U.S. Internal Revenue Tax Refunds. Index.

参考文献

[1]  Arnold L. Sweet,et al.  Computing the variance of the forecast error for the holt‐winters seasonal models , 1985 .

[2]  E. S. Gardner,et al.  FORECASTING WITH EXPONENTIAL SMOOTHING: SOME GUIDELINES FOR MODEL SELECTION , 1980 .

[3]  Victor Solo,et al.  The Order of Differencing in ARIMA Models , 1984 .

[4]  H. Iemoto.  Modelling the persistence of conditional variances , 1986 .

[5]  M. L. Goodman,et al.  A New Look at Higher-Order Exponential Smoothing for Forecasting , 1974, Oper. Res..

[6]  Peter R. Winters,et al.  Forecasting Sales by Exponentially Weighted Moving Averages , 1960 .

[7]  R. Meyer,et al.  The Fundamental Theorem of Exponential Smoothing , 1961 .

[8]  H. Akaike.  A new look at the statistical model identification , 1974 .

[9]  S. M. Wu,et al.  Exponential Smoothing as a Special Case of a Linear Stochastic System , 1974, Oper. Res..

[10]  G. C. Tiao,et al.  Consistent Estimates of Autoregressive Parameters and Extended Sample Autocorrelation Function for Stationary and Nonstationary ARMA Models , 1984 .

[11]  Dean W. Wichern,et al.  Assessing the Impact of Market Disturbances Using Intervention Analysis , 1977 .

[12]  S. Zabell,et al.  Babies and the blackout: the genesis of a misconception , 1981 .

[13]  R. Cook.  Influential Observations in Linear Regression , 1979 .

[14]  Murat Kulahci,et al.  Quality Quandaries: Studying Input-Output Relationships, Part I , 2006 .

[15]  G. Yule.  On a Method of Investigating Periodicities in Disturbed Series, with Special Reference to Wolfer's Sunspot Numbers , 1927 .

[16]  Marcus O'Connor,et al.  Artificial neural network models for forecasting and decision making , 1994 .

[17]  Douglas C. Montgomery,et al.  Forecasting and Time Series Analysis (2nd ed.). , 1992 .

[18]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[19]  C. Granger,et al.  Experience with Forecasting Univariate Time Series and the Combination of Forecasts , 1974 .

[20]  M. Bartlett.  On the Theoretical Specification and Sampling Properties of Autocorrelated Time‐Series , 1946 .

[21]  Douglas C. Montgomery.  Adaptive Control of Exponential Smoothing Parameters by Evolutionary Operation , 1970 .

[22]  M. Kendall,et al.  A Study in the Analysis of Stationary Time-Series. , 1955 .

[23]  G. Box,et al.  Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models , 1970 .

[24]  Douglas C. Montgomery,et al.  Modeling and Forecasting Time Series Using Transfer Function and Intervention Methods , 1980 .

[25]  C. Chatfield,et al.  Prediction intervals for multiplicative Holt-Winters , 1991 .

[26]  D. Cochrane,et al.  Application of Least Squares Regression to Relationships Containing Auto-Correlated Error Terms , 1949 .

[27]  R. H. Myers.  Classical and modern regression with applications , 1986 .

[28]  Everette S. Gardner,et al.  Exponential smoothing: The state of the art , 1985 .

[29]  Johannes Ledolter,et al.  Statistical methods for forecasting , 1983 .

[30]  P. A. Blight.  The Analysis of Time Series: An Introduction , 1991 .

[31]  R. Clemen.  Combining forecasts: A review and annotated bibliography , 1989 .

[32]  K. French,et al.  Expected stock returns and volatility , 1987 .

[33]  J. Ledolter,et al.  Evaluating the Impact of the 65 mph Maximum Speed Limit on Iowa Rural Interstates , 1996 .

[34]  Orazio Attanasio,et al.  Risk, Time-Varying Second Moments and Market Efficiency , 1991 .

[35]  J. Muth.  Optimal Properties of Exponentially Weighted Forecasts , 1960 .

[36]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[37]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[38]  M. L. Shone,et al.  Exponential Smoothing with an Adaptive Response Rate , 1967 .

[39]  G. C. Tiao,et al.  Model Specification in Multivariate Time Series , 1989 .

[40]  G. Reinsel.  Elements of Multivariate Time Series Analysis, 2nd Edition , 1998 .

[41]  Daniel B. Nelson.  CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH , 1991 .

[42]  R. Engle,et al.  Multivariate Simultaneous Generalized ARCH , 1995, Econometric Theory.

[43]  C. Chatfield,et al.  Prediction intervals for the Holt-Winters forecasting procedure , 1990 .

[44]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[45]  G. C. Tiao,et al.  Modeling Multiple Time Series with Applications , 1981 .

[46]  G. Box,et al.  On a measure of lack of fit in time series models , 1978 .

[47]  E. McKenzie.  General exponential smoothing and the equivalent arma process , 1984 .

[48]  George E. P. Box,et al.  Intervention Analysis with Applications to Economic and Environmental Problems , 1975 .

[49]  D. Cox.  Prediction by Exponentially Weighted Moving Averages and Related Methods , 1961 .

[50]  G. M. Jenkins,et al.  TESTS OF HYPOTHESES IN THE LINEAR AUTOREGRESSIVE MODEL: II. NULL DISTRIBUTIONS FOR HIGHER ORDER SCHEMES: NON-NULL DISTRIBUTIONS , 1956 .

[51]  James N. Bodurtha,et al.  Testing the CAPM with Time-Varying Risks and Returns , 1991 .

[52]  Ronald Eddy C O Eastma Swanson,et al.  Practical experiences with modelling and forecasting time series , 1980 .

[53]  Bovas Abraham,et al.  Some comments on the initialization of exponential smoothing , 1984 .

[54]  M. H. Quenouille.  Approximate Tests of Correlation in Time‐Series , 1949 .

[55]  J. Durbin,et al.  Testing for serial correlation in least squares regression. I. , 1950, Biometrika.

[56]  E. McKenzie,et al.  Error analysis for winters' additive seasonal forecasting system , 1986 .

[57]  Ruey S. Tsay,et al.  IDENTIFYING MULTIVARIATE TIME SERIES MODELS , 1989 .

[58]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[59]  Murat Kulahci,et al.  Quality Quandaries: Studying Input-Output Relationships, Part II , 2006 .

[60]  Helmut Ltkepohl,et al.  New Introduction to Multiple Time Series Analysis , 2007 .

[61]  M. Stella Atkins.  A Case Study on the Use of Intervention Analysis Applied to Traffic Accidents , 1979 .

[62]  R. Chou,et al.  ARCH modeling in finance: A review of the theory and empirical evidence , 1992 .

[63]  W. Fuller,et al.  Introduction to Statistical Time Series (2nd ed.) , 1997 .

[64]  Jammalamadaka.  Introduction to Linear Regression Analysis (3rd ed.) , 2003 .

[65]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

[66]  William W. S. Wei,et al.  Time series analysis - univariate and multivariate methods , 1989 .

[67]  C. Chatfield,et al.  The M2-competition: A real-time judgmentally based forecasting study , 1993 .

[68]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[69]  Chris Chatfield,et al.  Holt‐Winters Forecasting: Some Practical Issues , 1988 .

引用
Quality Quandaries: Box-Cox Transformations and Time Series Modeling—Part II
2008
Googling environmental issues: Web search queries as a measurement of public attention on environmental issues
Internet Res.
2016
A Comparison of Pedestrian Mobility Prediction Schemes in Wireless Cellular Networks
2015
Wireless Traffic Usage Forecasting Using Real Enterprise Network Data: Analysis and Methods
IEEE Open Journal of the Communications Society
2020
Comparison of Lavenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization Backpropagation Algorithms for Multistep Ahead Wind Speed Forecasting Using Multilayer Perceptron Feedforward Neural Network
2015
Prediction of wind speed time series using modified Taylor Kriging method
2010
Forecasting for Inventory Planning under Correlated Demand
2011
Smoothing inventory decision rules in seasonal supply chains
Expert Syst. Appl.
2016
Memetic Approaches for Optimizing Hidden Markov Models: A Case Study in Time Series Prediction
ACAL
2009
MPM: Map Based Predictive Monitoring for Wireless Sensor Networks
Autonomics
2009
Event Forecasting with Pattern Markov Chains
DEBS
2017
The Hybrid Forecasting Method SVR-ESAR forCovid-19
medRxiv
2020
Compression of time series by extracting major extrema
J. Exp. Theor. Artif. Intell.
2011
Enterprise System Response Time Prediction Using Non-stationary Function Approximations
IWANN
2019
Detection of temporal changes in psychophysiological data using statistical process control methods
Comput. Methods Programs Biomed.
2012
Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques
Energies
2019
Representing Job Scheduling for Volunteer Grid Environment using Online Container Stowage
2016
Towards Unsupervised Data Quality Validation on Dynamic Data
EDBT/ICDT Workshops
2020
Attack Classification Schema for Smart City WSNs
Sensors
2017
A Hybrid Framework for Topology Identification of Distribution Grid With Renewables Integration
IEEE Transactions on Power Systems
2020