time series

A nonlinear time series analysis method has been used for reconstruction of sea surface temperature (SST) anomalies .

To discuss if it is possible to forecast hemorrhagic fever with renal syndrome (HFRS) incidence with time series predictive models.

Time series are data generated by the dynamical systems, they always can be used to understand the underlying mechanisms.

Fourier transform is a way of looking at a time series as a sum of sine waves at different frequencies.

We establish the combined regression-time-series model for Chinese inflation and compare its forecast results to that of regression model.

Little bits of fairy dusts shimmering worldwide with a counter up top and an hourly time series chart on the bottom.

The external characteristic of many non-linear systems is only a certain form time series of variable usually.

A time series algorithm requires that the column or columns to be predicted must be continuous .

The results show that this approach of trend extrapolation better fitted the application on time series of ecological footprints.

In this paper, we extract rules of the decision table by an incremental algorithm for the dynamic decision table of the time series data.

In this paper, the Multivariate Autoregressive Model( MARM ) in time series is applied to set up the movement state of naval vessel .

Accordingly, the exploration to time series analysis has always been one of the hot research topics.

The result showed that this method was feasible for estimating the passed value and forecasting the coming value of the time series.

To see a time series plot of the meteorological readings in the past 24 hours, please click the mouse on the station of your choice.

Then sample time series was chosen which had the same property with predicting object.

RESULTS: Multiple canonical correlation analysis is able to map several time series to a consensus time scale.

The subject of time series is of considerable interest, especially among researchers in econometrics, engineering, and the natural sciences.

It provides tools for empirical work with time series data and is an introduction into the theoretical foundation of time series models.

It is typically used for time-series-based data gathering, but is not restricted to this; static data can also be used.

Finally, I predict financial risk situation for the next two years in Gansu province by use of AR autoregressive time series model.

Curve Fitting Method of Time Series Model can be used to predict the number of inbound tourists and tourism revenue of Chongqing.

Tab displays the behavior of a time series, together with the predicted values over time.

For more information about this algorithm, see Microsoft Time Series Algorithm.

The data collected by the loop detector, have the characteristics of time series, and have much to do with the detector's location.

The full analysis sheets for each stock file including a time series graph with the number of data points for each day.

Method: The relative degree analysis for multiple attribute decision making with time series was used to assess the project comprehensively.

A time series is a set of observations made at a succession of equally spaced points in time.

Finally, as an application of time series analysis, the short-term prediction to the sound pressure oscillation is presented and discussed.

In terms of time series fitting model annual increasing amount of Changsha's paramedics is obtained.

In this chapter we discuss one of the few abundant sources for temporal information, time series expression data.

By spectral analyzing, the overall period can be calculated, thus the holistic characteristics of hydrological time series can be reflected.

High frequency time series is referred to financial data which is sampled with interval of one hour, one minute even one second.

The Mining Accuracy Chart tab cannot be used with time series models or with models that have continuous predictable attributes.

In particular, it was assumed that the underlying model of the nonlinear time series was known, but the corresponding parameters were not.

Time series analysis is an important method of the dynamic data analysis which has extensive researches and usages in many subject fields.

As the time series, the gradual stability of community microclimate is a sign for the gradual maturity of community restoration.

To address the problem, scholars have put forward a time-series analysis and Kalman filter hybrid algorithm combining.

The method can be applied to the forecasting of time series in various engineering practices.

Objective To explore the application of seasonal time series ARIMA model in prediction of malaria incidence in an unstable malaria area.

Combined with a progression arithmetic, time series analysis of rock pressure in a tunnel i.

Data mining in the time series similarity search was applied to hydrological time series data for mining similar hydrological processes.

Finally, we give Bayesian analysis of the long memory time series ARFIMA model.

Research and teaching: econometric theory, non-parametric econometrics, nonlinear time series analysis, and financial econometrics.

Time series estimates showed improved control of high total serum cholesterol over the past two decades in England and the United States.

The time series of temperature are expected to be the longest possible so as to examine various modes of oscillation periods.

Because a time series model can contain multiple keys, both TimeIndex and ModelRegion are designated to be key columns.

Volatility has been one of the most active areas of research in empirical finance and time series econometrics during the past decade.

Through the analysis of time-series change, the author has discovered that both absolute difference and relative difference had extended.

In the first case, the length of the time series, the frequency and the estimation methodology may lead to different estimates.

Engineering structure parameter identification method applied with time series ARMA modeling under ambient excitation was researched.

The experiment results on the stock forecasting show that the method is efficient, even for the time series with large fluctuations.

According to the characteristics of the original data, the random time series analysis is chosen to be the analysis method.

The Microsoft Time Series algorithm supports several parameters that affect the performance and accuracy of the resulting mining model.

Method Explaining changes of hospital indexes through drawing time series sequences and computing partial correlation coefficients.

Formally, a sequence of random variables indexed by time is called a stochastic process or a time series process ?

With the Charts tab of the Microsoft Time Series Viewer, you can investigate the time series that the algorithm creates.

Then it makes a study and forecast of the earnings rate of five-year stock investment in U S stock market by time series analysis.

Enders continues to provide business professionals with an accessible introduction to time-series analysis.

Objective Establishing a trend prediction model of time series on drug consumption to e value the application effective of the model.

The analysis and modeling of the financial time series is a very important study realm in financial metrology.

Vegetation index time-series data has been widely used in land cover classification and ecological regionalization.

This report in Linyi city high school for the sample survey, the focus of city in all high school within the time-series method.

The results show that the change of correlation dimension of creep measurement time series reflects the different tectonic movement.

A state space approach for the modeling of nonstationary time series is presented.

Electricity load can be seen as a series of random sequences, their characteristics can be analyzed by time series.

Information collected in accordance with the time series, multi-year plan to the countries to start the main line.

Finally, we use time series method to predict the output of the next few years.

As a general method, the scheme has major theoretic value and wide application for predicting of the nonlinear time series.

Secondly, the outlier pattern of time series can be detected by local outlier detection technique.

At last, after the analysis of an actual case, the reliability and operability of time series forecasting method is proved.

Objective: Fit the forcast model that suit to the time series data of the Hospitalization Expenses of Injury Children.

'The key constraint was the lack of consistent time series of data on credit risk. '

Longitudinal data is different for each individual observation time obtained by the cross-section and time series data together.

Markov-switching model is a method applied to investigating the structural changes of time series.

The kernel function can be unit hydrograph of deterministic system or time series model of stochastic system.

Secondly, time series trend analysis models in common use are introduced, whose illative process and applicability are also expatiated.

Under certain condition by this method the problem of losing many data for time series processing can be resolved.

Specifies the minimum number of time slices that are required to generate a split in each time series tree.

Due to incipient fault features of gear being not obvious, the parameter index of time series model method was researched.

Historical prediction is limited by the following Microsoft Time Series algorithm parameters HISTORIC_MODEL_COUNT and HISTORICAL_MODEL_GAP.

This dissertation focuses on the application of SVR and its extension in predicting stock index time series.

The generalized autoregressive conditional heteroscedasticity (GARCH) model has the ability to describe the volatility of time series.

Statistical data analysis, time series analysis, and error estimation will be discussed in the context of each lab.

Also, finical time series, which marginal density distribution have higher excess kurtosis, typically exhibit the feature of heavy tail.

In the last part we give an extended neoclassical growth model, and use time-series data of China economic transition.

"The most recent 11 summers have all experienced melting greater than the average 35 year time series, " he said.

Using high frequency intra - day data, we study the correlations between liquidity and trading activity and their time series property .

Analysis report of station history and re-calibration of time series as required due to sensor and location changes.

Finally, we present methods for combining time series expression data with static data to reconstruct dynamic regulatory networks.

Proposed dynamic clustering algorithm has strong robustness in clustering of time series multi-dimensional data.

Time series data with observations recorded for each period in an associated calendar.

Stock market price prediction is regarded as a challenging task of the financial time series prediction process.

As the important area of data mining research, time-series data mining and forecasting have developed greatly.

First, the users' needs are defined as the event and the original time series is translated into the event sequence.

Two algorithms for structuring decision tree model of multi-dimensional time series classification were presented.

We have to draw statistical inference by hypothesis testing, because we don't know the real data generation process of any time series.

As a general method, the scheme has major theoretic valve and wide-ranging application for predicting of nonlinear time series.

non-Gaussian model reveals that the Gaussian structural time series method is robust for seasonal adjustment.

In the perspective of time series, shares of foreign investment in most subordinate industry fluctuate remarkably.

For multiple stationary time series Granger causality tests and vector autoregressive models are presented.

Time series data is continuous and can be stored in a nested table or in a case table.

Information collected in accordance with the time series, multi-year plan to expand the main line.

In this paper, we discuss the applications of time series modeling method in the analysis of lubricating oil of mechanical equipment .

The paper discusses modelling principles and predicting methods of grey system analysis model and time series analysis model.

This theory is extended to the multidimensional impact of this factor and other financial time series in this article also.

Objective To explore the application of heteroscedastic time series model to the analysis of data of infectious diseases.

A new segmenting algorithm of time series was proposed to satisfy the requirement of processing chromatogram data of well-logging.

RRDtool is an open source, high performance data logging and graphing system for time series data.

Currently, the traditional research methods or artificial neural network's single research methods are adopted in time series prediction.

The persistence definition and correlative character of high-frequency time series are given at the time.

Absrtact: As a probability and statistics methodology, linear time series has been applied to many fields.

Objective To fit the prediction model that suit to the time series data of the medical service demand.

For more information about using HISTORICAL_MODEL_COUNT and HISTORICAL_MODEL_GAP, see Microsoft Time Series Algorithm.

Conclusions AR-GARCH model is suitable for analyzing heteroscedastic time-series data of infectious diseases.

AR model is built by maximum entropy method on bearing running condition, and time series model of fault and normal condition is computed.

The time series of non-linear dynamics system is the main basis of studying complicated system.

By ADF-Test, this article reveals both FDI and economic growth of the Inner Mongolia are non-stationary Time Series.

The conditional heteroscedasticity is a typical characteristic of finance time series.

Graphite is a Python web application that has been developed to provide scalable storage and visualization for numeric time-series data.

Recently, the similarity-based pattern search of time series has received increasing attention.

The ARIMA model has been applied to evaluate and predict the time series of macroscopic traffic volume.

So if your big data infrastructure must cope with time series, Informix is more than up to the job.

When the financial time series is Gaussian distribution, the two methods of selection time delay will have the same result.

At last, Use algorithm of time series similarity search to find the similar history stock data with the given stock's direction.

he time series of instantaneous velocity in the flows are obtained by the hot-wire technique and data sampling.

Conclusion Time series decomposition is applicable of drug consumption prediction of h. . .

And ultra high frequency time series is tick-by-tick data, which contain plenty of market information.

To identify the obtained response time series, a discrete wavelet transform (DWT) technique was proposed.

Size curves of cocoon filaments can be regarded as non-stationary time series with finite length varying at random.

This viewer displays mining models that are built with the Microsoft Time Series algorithm.

There are many researches on univariate time series mining, whose mature theory and methods have been proposed.

This section focuses on how time series data can be monitored using control charts.

The evaluating of the approach. Appraise the advantage and deficiency of the method of random time series.

The results show that extreme short prediction of FPSO pitch motion using time series method is accurate and the calculation is simple.

In this paper, we apply multivariable fuzzy time series models to forecast numbers of teachers in vocational high school.

SampEn time series on the approximation of the laws of a variable is estimated.

For instance, we have to build time series views into the data becasue a lot of our analysis takes temporal factors into account.

Residual control chart is the method of using the residuals of time series models to control the quality of the auto-correlated process.

We introduce a as a base-scale entropy complexity measure for analyzing time series.

This paper analyzed and predicted the water demand of cotton in dry areas by using analysis of time series in Engineering Hydrology.

By calculating the longest common subsequence , finally, similarity searching in time-series data sets is realized.

Time Series Analysis (Time series analysis) is a dynamic statistical methods of data processing.

A new method that applies wavelet analysis and time series theory to air conditioning load forecasting was presented.

The research result has shown Chaos property and predictability of the time-series of the telephone traffic demand. 3.

You can define input data for the Microsoft Time Series model in two ways.

The Lag function can be used with time series models to return or filter on the time lag between each case and the initial time.

This paper proposes an improved adding-weight one-rank local-region method for prediction of chaotic time series.

The process of knowledge discovery in time series includes preprocessing of time series data, attributes reduction and rules extraction.

The multivariable fuzzy time series Heuristic model is the easiest method to follow.

The emergence of Copula provides a new tool to the related analysis and multivariable time series analysis.

The so-called time series prediction is forecast to reflect the changes of things past and present structure of the data time series model.

China's economic time series such as GDP, M1 and total retail sale of consumer goods are used to set up the State Space Models.

surface electromyography(sEMG) is a one dimensional time series signal of neuromuscular system recorded from skin surface.

We present a two-stage method for obtaining both phase and object estimates from phase-diversity time series data.

The example result shows that the random-sets method is a nice approach to time series analysis.

However, the Microsoft Time Series algorithm supports additional functions, listed in the following table.

Research and teaching: time series econometrics and empirical finance.

Cleanness coefficient of the condenser is predicted based on support vector regression(SVR) time series prediction method.

In this paper, we present a nonparametric approach for checking the residuals of time series models.

In this paper, by analyzing the structure of interpolation filter (DDF), the time series and interpolation filtering algorithm are combined.

First of all, By using the wind speed data measured in Jishan, I setup the model based on time series theory.

Most methods introduced in this paper are also useful for other time series forecasting problems.

The time series method ARIMA is introduced, so the trend forecast of insulation will be gotten effectively.

In this paper a fast algorithm is presented for recovering time-series patterns in a long sequence.

multivariable fuzzy time series models Two-factor model Heuristic model Markow model.

and the time series model of the longitude error and latitude error of navigation system is established and the verification is carried out.

The reliability of the time-series analysis method in processing unsteady random signals is verited.

A time series forecasting method based on dynamic weighted least squares support vector machine (LSSVM) is proposed.

This expression is consistent with the recent time series variation of some key macroeconomic variables.

An ARMA innovation model and the state optimal filter are designed by modern time series analysis method.

Financial time series analysis is more concerned on the rational economic interpretation than the statistical significance.

This paper introduced a method of wavelet based analysis and forecasting for non stationary time series.

The view that child labor declines with prosperity fits well with global time-series statistics.

We do not have time series on the number of elephants.

Si Yong electronic display products has always worked for a long time series of sound and display technology.

In this paper, we proposed an autoregressive time series model of log odds ratios to price derivatives.

Predicts the future values for a time series.

This kind of dynamic dependence of observations is the memory of time series.

Parameter estimation is the key in building up a model in time series analysis.

Absrtact: It is the most basic and frequent treatment that to carry on trend analysis to the time - series data.

Based on this design, algorithms are proposed for efficient analysis of time-series data streams.

One novelty of the paper is that it permits the presence of structural breaks that affect both the level and the slope of the time series.

RRD is a system to store and display time-series data (i. e. networkbandwidth, machine-room temperature, server load average).

Time series photographs of the droplet from heated to fully burned are given.

Each time series includes information about both sales quantity and sales amount.

Combing with the chaos theory and the wavelet analysis, the WNN model of traffic flow time series is built.

This article also recounted the measure of producing the time series for control.

This thesis surrounds the symbolic analysis of stock time series to spread research.

Based on the rub-impact fault levels and trends, a time series analysis technique, i.

The time series prediction of station elements is verified and analyzed by using the method suitable for time series verification.

Other modeling techniques include ANOVA, time series, and data mining.

The model is run as a time series.

A novel fuzzy quantitative data mining method based on time series is proposed for the fuzzy modeling of complex industry process.

A dynamic clustering algorithm was proposed based on consistent matrix of dependent function for time series multi-dimensional data.

in on - line diagnosis , time series analytic approach is used to take delivery of fault symptoms.

The short scaling time series have multifractal properties.

After that, we research on Incremental mining in time series with chained data structure, based on Entire-Segments Incremental Algorithm.

In addition, long term time series had long term dependency which could be identified by Hurst coefficients.

It is obvious that application of SVM in time series prediction of electric current will be of great significance.

Traditional linear cointegration theory and ECM can be used widely in time series analysis.

This is called a deseasonalizing time series.

The Eastern Conference leads the all-time series over the West 34-21 but no one really cares who wins the game.

Trend change detection has been applied widely to applications of time series stream.

At the same time, however, the nonlinear and chaotic characteristic of time-series data makes the mining be a difficult issue.

Using the analyzation of time series data, the relation of the pest population time series data and climate's factors was studied.

Yongmiao Hong, Nonlinear Time Series Analysis: Econometric Theory and Applications, 2007(unpublished).

In this paper, the similarity mining method for time series data is investigated.

Under the rationale of the time series forecast, the article produced a model that based on the time series support vector machine forecast.

Returns predicted future or historical values for time series data.

Reactor noise experiments by using acquisition system for time series data of pulse train.

In this paper, the local time series of cross visual cortex was proposed to solve the problem.

Hence the time series of the development models tend to be complete.

The method of time series is adopted in the paper, it is helpful to forecast on-line monitoring data tendency.

A combined dynamic method of forecast traffic volume time series is proposed.

The time series model is simple and has acceptable result in short-term prediction, and it is the common linear prediction model.

A novel fault prediction method based on multiple kernel-learning is proposed for fault prediction in nonlinear time series.

And this algorithm can be easy calculated and carry out, the training is needless and it can support the dynamic increase of time series.

Finally, the random error model of HRG is established by using time series analysis method.

In this task, you will explore time series describing sales in the regions Europe, North America, and the Pacific.

In this paper, the outlier mining method for time series data is investigated.

We analyze and forecast the capacity of market of Tibet drug by using Time Series Regression, and the result is exciting.

A method based on symbolic time series and entropy theory is proposed to analyse the nonlinear behaviours of DC-DC converters.

The results show that the prediction model of chaotic time series based on wavelet de-noising and LS-SVM is better.

The time series analysis approach is adopted to build its random error model.

The seasonal time series has the double trends of increasing and fluctuating.

Based on the data mining of the time series, generally discretizes time series, then clusters different sub-pattern.

The time series of network traffic data was modeled and forecasted based on Wavelet Neural Network (WNN).

In view of the importance of crude oil price forecasting, this paper study a new model for time series of crude oil price forecasting.

the checkout of time series analysis is residual test.

Once you have built up a time series, you will probably want to retrieve historical data.

When we measure the high dimension system, we usually acquire the one-dimension time series of this system.

In this paper, a prediction model of short-time traffic flow is presented based on the chaotic time series analysis.

Time series in the 200-yr simulation shows different trends in TN and TP concentration changes.

Part four: In previous descriptive statistics, based on the time-series on the use of econometric tools for empirical research.

The authors' emphasis throughout is on time series econometrics.

This paper proposes a new linear fitting method for time series.

The regression formula is used to predict values in the time series.

This paper proposes a new algorithm for mining frequent subsequence in time series based on symbolic representation.

A new time series prediction method was put forward, based on fuzzy least square support vector machine (FLS-SVM).

The most important purpose to analyze time series with transfer function model is improving the precision of forecast.

The monthly central tax revenues forecasting is considered based on time series method.

A routing algorithm based on time series prediction for delay tolerant network is proposed.

This paper presents an analytical method of monitoring data based on the time series modeling.

An on-line leak-detection method based on pressure time series of oil pipeline is proposed for leakage.

A new prediction model was proposed by Newton interpolation and neural network based on time series.

Charpter2: Effect of filters on singular value of the time series.

The existing methods of similarity search are not suitable for high frequency financial data, which is a kind of non-interval time series.

The future approximate prices were forecasted by multivariate time series models based on historical approximate prices and loads.

The time series representation KL method based on the important sub-point is studied.

This paper presents an outlier pattern detection algorithm of time series based on K-Mean Distance Outlier Factor (K-MDOF).