Segmentation algorithm for long time series analysis

Time series analysis is an important issue in the earth science-related engineering applications such as hydrology, meteorology and environmetrics. Inconsistency and nonhomogeneity that might arise in a time series yield segments with different statistical characteristics. In this study, an algorithm based on the first order statistical moment (average) of a time series is developed and applied on five time series with length ranging from 84 items to nearly 1,300. Comparison to the existing segmentation algorithms proves the applicability and usefulness of the proposed algorithm in long hydrometeorological and geophysical time series analysis.     

Multivariate hydrologic time series analysis

The objective of this study is to establish a multivariate watershed hydrologic system model involving meteorological data as the input and river flow as the output of the system. Monthly hydrological time series of runoff, temperature and precipitation were selected for analysis. A first-order autoregressive-moving average (ARMA) transfer function model was found adequate to describe the multivariate watershed hydrologic system for the monthly runoff and meteorological time series. The results also indicated that the coordinated use of the meteorological and hydrometric data would enhance the accuracy of estimation of runoff characteristics.     

Periodicity in time series of wind direction data

Summary The existence of periods of 24 and 92 hours has been proved on the basis of the analysis of time series of hourly mean wind direction data measured at the meteorological station in Praha-Ruzyn in the period from January to June, 1978. Besides, the closest mutual interdiurnal dependence of wind direction data observed at the climatological observation hour, at 7 a.m., has been proved. A method applicable to analysing time series of wind direction data by means of a computer is described.     

Spectral techniques for the analysis of tidal time series

Summary Three different methods are given for the Fourier analysis of time series leading to the determination of high reliable values of frequencies, amplitudes and phases of the inherent components. For two methods the hypothesis is made that the time series is sufficiently long to separate very close components, while for the third the frequencies are supposed to be given. Two of these methods have been applied to a time series obtained by sampling an artificial tidal function.     

Complexity analysis of riverflow time series

I have used the Lempel–Ziv measure to assess the complexity in riverflow activity over England and Wales for the period 1867–2002. In particular, I have examined the reconstructed monthly riverflow time series from fifteen representative catchments in these regions and calculated the Lempel–Ziv Complexity (LZC) value for each time series. The results indicate that the LZC values in some catchments are close to each other while in others they differ significantly. In addition, I have divided the period 1867–2002 into four equal subintervals: (a) 1867–1900, (b) 1901–1934, (c) 1935–1968, (d) 1969–2002, and calculated the LZC values for the various time series in these subintervals. It is found that during the period 1969–2002, there is a decrease in complexity in most of the catchments in comparison to the subinterval 1935–1968. This complexity loss may be attributed to increased human intervention involving land and crop use, urbanization, commercial navigation and climatic changes due to human activity. Determining the complexity in the riverflow time series is important because an understanding of the extent of complexity may be useful in developing appropriate models of riverflow activity. The extent of complexity may also influence the predictability of the variability in riverflow dynamics.     

Error analysis for the evaluation of model performance: rainfall–runoff event time series data

This paper provides a procedure for evaluating model performance where model predictions and observations are given as time series data. The procedure focuses on the analysis of error time series by graphing them, summarizing them, and predicting their variability through available information (recalibration). We analysed two rainfall–runoff events from the R‐5 data set, and evaluated 12 distinct model simulation scenarios for these events, of which 10 were conducted with the quasi‐physically‐based rainfall–runoff model (QPBRRM) and two with the integrated hydrology model (InHM). The QPBRRM simulation scenarios differ in their representation of saturated hydraulic conductivity. Two InHM simulation scenarios differ with respect to the inclusion of the roads at R‐5. The two models, QPBRRM and InHM, differ strongly in the complexity and number of processes included. For all model simulations we found that errors could be predicted fairly well to very well, based on model output, or based on smooth functions of lagged rainfall data. The errors remaining after recalibration are much more alike in terms of variability than those without recalibration. In this paper, recalibration is not meant to fix models, but merely as a diagnostic tool that exhibits the magnitude and direction of model errors and indicates whether these model errors are related to model inputs such as rainfall. Copyright © 2004 John Wiley & Sons, Ltd.     

小波分析在GPS位移时间序列特征中的应用

使用福建GPS台网2004年3月—2008年10月的连续观测资料,对各基准地震站原始数据进行处理,得到位移时间序列,进行初步分析。在此基础上,采用小波分析方法提取时间序列的时频特征,从而得到非线性变化信息,为GPS在大地震前获取前兆资料提供探索方向,进而通过对位移时间序列异常变化与区域地壳运动关系的研究,探讨两者间的内在联系。     

Network analysis methods of heliorelated time series

In this work, we present the diagnostics results of the similarity between different temperature paleoreconstructions using network approaches. The correlation patterns of time series are transformed into the geometry of the corresponding graph, which can be analyzed geometrically. To detect a possible nonlinear connection between climatic series and solar activity, we use networks constructed by embedding time series in the space of an appropriate dimension. Finally, we present Markov networks for climatic reconstructions and annual Wolf numbers.     

Spectral analysis for GNSS coordinate time series using chirp Fourier transform

Spectral analysis for global navigation satellite system (GNSS) coordinate time series provides a principal tool to understand the intrinsic mechanism that affects tectonic movements. Spectral analysis methods such as the fast Fourier transform, Lomb–Scargle spectrum, evolutionary power spectrum, wavelet power spectrum, etc. are used to find periodic characteristics in time series. Among spectral analysis methods, the chirp Fourier transform (CFT) with less stringent requirements is tested with synthetic and actual GNSS coordinate time series, which proves the accuracy and efficiency of the method. With the length of series only limited to even numbers, CFT provides a convenient tool for windowed spectral analysis. The results of ideal synthetic data prove CFT accurate and efficient, while the results of actual data show that CFT is usable to derive periodic information from GNSS coordinate time series.     

The analysis of some monthly hydrologic time series

The paper presents the analysis of the following time series: monthly average temperatures at Urbana, Illinois (1915–1965); monthly precipitations at Morrisonville, Illinois (1896–1969); and monthly streamflows in the Sangamon River at Monticello, Illinois (1915–1969).The identification of a model for these series had been discussed in a previous paper (Torelli and Chow, 1972). In the latter it was shown that a simple transformation makes the series stationary.The analysis of the series is completed in the present paper. By a joint use of spectral and regression analysis one arrives at the formulation of models in which the deterministic process and the variances are described by means of trigonometric functions. This allows a considerable economy of parameters in comparison with the models based on the 24 sample monthly averages and variances.The practical advantages of such an economy of parameters are discussed.     

Periodograms of superimposed epochs in search for hidden rhythms in experimental data time series

Three methods for calculating the periodograms of superimposed epochs, designed for revealing and investigating periodic components with nonharmonic signal forms are described. Examples of periodograms and Fourier spectra calculated for model time series with known properties are presented. Comparative efficiency of the methods for signals of different forms is discussed. The results of analysis of different experimental signals will be compared in a separate paper that will be a sequel to this work on the optimized choice of methods for analysis of periodic processes.     

Similarity search and pattern discovery in hydrological time series data mining

The rapid development of data mining provides a new method for water resource management, hydrology and hydroinformatics research. In the paper, based on data mining theory and technology, we analyse hydrological daily discharge time series of the Shaligunlanke Station in the Tarim River Basin in China from the year 1961 to 2000. Firstly, according to the four monthly statistics, namely mean monthly discharge, monthly maximum discharge, monthly amplitude and monthly standard deviation, K‐mean clustering was used to segment the annual process of the daily discharge. The clustering result showed that the annual process of the daily discharge can be divided into five segments: snowmelt period I (April), snowmelt period II (May), rainfall period I (June–August), rainfall period II (September) and dry period (October–December and January–March). Secondly, dynamic time warping (DTW), which is a different distance metric method from the traditional Euclidian distance metric, was used to look for similarities in the discharge process. On the basis of the similarity matrix, the similar discharge processes can be mined in each period. Thirdly, agglomerative hierarchical clustering was used to cluster and discover the discharge patterns in terms of the autoregressive model. It was found that the discharge had a close relationship with the temperature and the precipitation, and the discharge processes were more similar under the same climatic condition. Our study shows that data mining is a feasible and efficient approach to discover the hidden information in the historical hydrological data and mining the implicative laws under the hydrological process. Copyright © 2010 John Wiley & Sons, Ltd.     

Land cover mapping using time series HJ-1/CCD data

It is very difficult to have remote sensing data with both high spatial resolution and high temporal frequency; thus, two categories of land-use mapping methodology have been developed separately for coarser resolution and finer resolution data. The first category uses time series of data to retrieve the variation of land surface for classification, which are usually used for coarser resolution data with high temporal frequency. The second category uses fine spatial resolution data to classify different land surface. With the launch of Chinese satellite constellation HJ-1in 2008, four 30 m spatial resolution CCDs with about 360 km coverage for each one onboard two satellites made a revisit period of two days, which brought a new type of data with both high spatial resolution and high temporal frequency. Therefore, by taking the spatiotemporal advantage of HJ-1/CCD data we propose a new method for finer resolution land cover mapping using the time series HJ-1/CCD data, which can greatly improve the land cover mapping accuracy. In our two study areas, the very high resolution remote sensing data within Google Earth are used to validate the land cover mapping results, which shows a very high mapping accuracy of 95.76% and 83.78% and a high Kappa coefficient of 0.9423 and 0.8165 in the Dahuofang area of Liaoning Province and the Heiquan area of Gansu Province respectively.     

Comparative analysis of fuzzy inference systems for water consumption time series prediction

Two types of fuzzy inference systems (FIS) are used for predicting municipal water consumption time series. The FISs used include an adaptive neuro-fuzzy inference system (ANFIS) and a Mamdani fuzzy inference systems (MFIS). The prediction models are constructed based on the combination of the antecedent values of water consumptions. The performance of ANFIS and MFIS models in training and testing phases are compared with the observations and the best fit model is identified according to the selected performance criteria. The results demonstrated that the ANFIS model is superior to MFIS models and can be successfully applied for prediction of water consumption time series.     

Singular spectrum analysis and forecasting of hydrological time series

The singular spectrum analysis (SSA) technique is applied to some hydrological univariate time series to assess its ability to uncover important information from those series, and also its forecast skill. The SSA is carried out on annual precipitation, monthly runoff, and hourly water temperature time series. Information is obtained by extracting important components or, when possible, the whole signal from the time series. The extracted components are then subject to forecast by the SSA algorithm. It is illustrated the SSA ability to extract a slowly varying component (i.e. the trend) from the precipitation time series, the trend and oscillatory components from the runoff time series, and the whole signal from the water temperature time series. The SSA was also able to accurately forecast the extracted components of these time series.     

天气和气候的时间序列特征分析

本文从天气和气候资料出发,提出气候的q阶（0≤q≤1）微商是天气,而天气可以近似为白噪声.在此基础上,利用描述自相似非马尔可夫随机过程的时间分数维扩散方程的分析成果,并结合时间序列的相关性分析,从理论上进一步指出气候信号的记忆性好于天气信号,且其概率密度分布的尾巴比较长.     

Operational Advanced Air Pollution Modeling

— Operational models that use solutions of the advection-diffusion equation based on more realistic assumptions than that of homogeneous wind and eddy diffusivity coefficients are presented. In particular a new parameterization for a model using a solution that accepts wind and eddy diffusivity profiles described by power functions of height is introduced. The performance of the model with the new parameterization was assessed using experimental data sets.     

基于电磁探测数据的时序分析模型研究

针对电磁探测数据交叉检验时对不同卫星探测数据的时间匹配需求,本文基于DEMETER卫星时序探测数据,分析了国际参考电离层(IRI)模型模拟电子浓度(N_e)数据在不同纬度区域的误差分布特征; 同时,基于自回归移动平均(ARIMA)模型构建了N_e数据时序预测模型. 在此基础上,分析比较IRI模型与ARIMA模型在N_e数据时序预测中的优缺点,结果表明: ARIMA模型模拟预测N_e数据时间序列的相对误差在短期内较低(小于10%),且随着预测时间的增长而增大; 而IRI模型模拟预测N_e数据时间序列的相对误差不会随着预测时间的增长而增大,且在高纬度地区的预测相对误差比在中低纬度地区低.      

Multi-step SETARMA predictors in the analysis of hydrological time series

The performance of the self-exciting threshold autoregressive moving average model in forecasting river flow data is investigated. Multi-step forecasts of two daily time series are generated through three different nonlinear predictors. The model adequacy to capture the main features of the data under study and its forecasting performance are analysed and discussed.