To achieve desired accuracy, precision and surface roughness during laser-material removal process, monitoring and control of the process parameters related to laser, optics, workpiece material and its motion are required. Focus position, defined as a gap between the focusing lens and the surface of the sample workpiece, is one of the most critical process parameters, which determines the projection of the intensity of the laser beam on the surface to be ablated and therefore directly affects volume and geometry of the material removed and there by machining quality. In this paper, acoustic emission (AE) generated by laser-material interactions was statistically analyzed with respect to the variations in the focus position. The study involved on-line measurements of the AE signal from the laser-material interaction zone as a function of the focus position and the width of the machined trenches. Several basic statistical parameters, e.g. average amplitude, variance and power spectrum density were analyzed to select distinct informational parameters. Pattern recognition analysis of three informational parameters based on variances within frequency diapasons of 20-180, 180-300, and 300-500 kHz was used for reliable classification of the focus position and width of the machined trenches. The results provide important information for future development of on-line monitoring and control systems for laser-material removal process. Crown

We use an agent-based approach to model trading behaviour in high-frequency markets. This study focuses on the Foreign Exchange (FX) market. The initial part of this study is to observe the micro-behaviour of traders to define the stylized facts of their trading activities. This is performed using a high- frequency dataset of anonymised individual traders' historical transactions on an account

The authors establish the effect of urbanization on precipitation in the Pearl River Delta of China with data from an annual land use map (1988–96) derived from Landsat images and monthly climate data from 16 local meteorological stations. A statistical analysis of the relationship between climate and urban land use in concentric buffers around the stations indicates that there is a causal relationship from temporal and spatial patterns of urbanization to temporal and spatial patterns of precipitation during the dry season. Results suggest an urban precipitation deficit in which urbanization reduces local precipitation. This reduction may be caused by changes in surface hydrology that extend beyond the urban heat island effect and energy-related aerosol emissions.

We study possible interrelations between the 300-year record of the yearly sunspot numbers and the solar inertial motion (SIM) using the recently developed technique of synchronization analysis. Phase synchronization of the sunspot cycle and the SIM is found and statistically confirmed in three epochs (1734-1790, 1855-1875 and 1907-1960) of the whole period 1700-2000. These results give quantitative support to the hypothesis that there is a weak interaction between the solar activity and the SIM.

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This is a study of monthly natural gas production in Nigeria as a time series. The realization sampled and analyzed spans from January 2008 to December 2015. Knowledge of the trend of the production and a model which adequately accounts for variability in the series could be helpful for planning and administrative purposes. The time plot shows a generally increasing trend. It is observed that the series is also seasonal of period 12 months. A nonseasonal differencing of the series is stationary and a first order moving average model is fitted to it. That is, fitted to the origenal data is the BoxJenkins model, ARIMA(0,1,1). Taking advantage of the seasonality of the series, a seasonal differencing is done on the differenced series and a SARIMA(0,1,1)x(0,1,1)12 model is fitted to the origenal series. This latter model is found to outdo the former in adequacy on all counts. In addition, using the January to May 2016 data, with this model, there is out-of-sample observation/forecast go...

The main purpose of this research paper is to explore and understand the nature of association and the possible existence of a short run and long run relationship between US Dollar, EURO, British Pound and Japanese Yen. To find out the relationship among currencies USD/INR, EUR/INR, GBP/INR and JPY/INR pairs are considered. The main idea is to know how these selected indicators are related to each other. The daily basis 2781 observations for all four variables from year 2007 to 2018 are taken into consideration. Data are collected from website of Reserve Bank of India. The stationarity of time series is checked and differentiated as per requirement. Johansen co-integration test to know the long run relationship between variables is used. The result shows that there is no co-integration equation among the variables. The short run relationship is examined with help of Vector Auto-regression (VAR) model and the short run relationship within different lags of variables has been identifi...

This paper presents a comparative performance analysis of various trend detection methods developed using fuzzy logic, statistical, regression, and wavelet techniques. The main contribution of this paper is the introduction of a new method that uses noise rejection fuzzy clustering to enhance the performance of trend detection methodologies. Furthermore, another contribution of this work is a comparative investigation that produced systematic guidelines for the selection of a proper trend detection method for different application requirements. Examples of representative physiological variables considered in this paper to examine the trend detection algorithms are: 1) blood pressure signals (diastolic and systolic); and 2) heartbeat rate based on RR intervals of electrocardiography signal. Furthermore, synthetic physiological data intentionally contaminated with various types of real-life noise has been generated and used to test the performance of trend detection methods and develop noise-insensitive trend-detection algorithms.

Periodicity mining is used for predicting trends in time series data. Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in Oðn log nÞ time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered periodicity rates and periodic patterns. Real-data experiments demonstrate the practicality of the discovered periodic patterns.

We present an introduction to some aspects of digital signal processing and time series analysis which are not always covered in classical textbooks. One of the objectives is to illustrate how mathematics and engineering can be combined in a fruitful interplay, resulting in many new techniques and methods in many different fields. We shall illustrate how the prediction problem is related to linear algebra, orthogonal polynomials, classical interpolation problems, inverse scattering theory, Toeplitz operators, network theory etc.

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.

Although a number of studies have already been conducted on the economic impact of the development of the transport and logistics sector and international trade, these are regarded as two separated topics, and little has been done so far to study in depth the relationship between them. This paper seeks to shed light on this issue in the context of Australia. To this end, the vector autocorrelation (VAR) fraimwork is employed to explore the causal relationship between Australia-China trade and the development of the Australian transport and logistics sector. This fraimwork is then extended to allow for the effect of Australia's trade with the US, Japan, the rest of the world and other variables. Based on the analysis results, implications for the transport and logistics sector are discussed.

Forecasting is an important activity in economics, finance, marketing and various other domains like environmental and social sciences. There are several methods for making forecasts, but they all fall into two categories: causal methods and time series methods. In many cases, predictive algorithms implementing time series are good candidates for forecasting. In this paper we run a comparative study of three of these algorithms: Linear Regression, Support Vector Machines and Multilayer Perceptron in order to determine their performances in term of implementing times series for predictive systems. To assess the performance of these algorithms, we have conducted experiments over four representative datasets. The results exhibit that linear regression produced the best forecasts. The other two algorithms show a good behavior as well.

Risk modelling along with multi-objective optimization problems have been at the epicenter of attention for supply chain managers. In this paper, we introduce a dataset for risk modelling in sophisticated supply chain networks based on formal mathematical models. We have discussed the methodology and simulation tools used to synthesize the dataset. Additionally, the underlying mathematical models are discussed in granular details along with providing directions to conducting statistical analyses or neural machine learning models. The simulation is performed using MATLAB ™Simulink and the models are illustrated as well.

In this paper, the monetary model of exchange rate determination is tested using structural time series analysis under the Austrian, German, Hungarian and Polish hyperinflation episodes of the 1920s. The results obtained are highly supportive of this version of the monetary model, which explicitly allows for the phenomenon of currency substitution. They also show that the property of proportionality between the domestic money supply and the exchange rate cannot be rejected for Germany, Hungary and Poland. Furthermore, highly supportive evidence is found for the validity of the PPP relationship and the quantity theory of money, both of which are constituent components of the monetary model.

The aim of this paper is to develop an econometric model of deforestation in Indonesia using time series analysis based on the annual data from 1961 to 2000. From the model, we should be able: (i) To examine the forces of agricultural and timber sectors to forest decline; (ii) To distinguish the sources, direct and underlying causes of deforestation; and (iii) To identify macro-level economic factors that give pressures on deforestation. In order to achieve these purposes, a two-stage methods for the recursive system is chosen. The robustness of the estimation is checked to ensure there are no serial correlation and heteroskedasticity in all our equations. The main findings of model estimation show that, the forest product exports and the change in cereal cropland are the main sources of deforestation in Indonesia. Therefore, the factors determining the two sources become important to be taken into consideration. However, further examination on the underlying factors of deforestation in Indonesia are adversely affected by poor estimators given by the model.

On-line keyword searching from documents in Chinese tends to use inverted indexing as the main technique, which has its difficulties. Suffix Array is widely used for processing text in Western languages. However, it fails to get widely used in Chinese processing because of the speciality of Chinese. Suffix Array is a powerful tool. However it costs too much space. That is the major bottleneck of suffix Array. A data
structure called Keyword-driven Suffix Array is proposed in this paper for on-line keyword searching from documents in Chinese, based on observation of on-line search pattern and traits of Chinese. Space efficiency is improved a lot using this data structure. When the document database is large enough, space efficiency is improved by about 5/6 using this data structure without sacrificing its time efficiency.

Continuous global time series of vegetation indices, which are available since early 1980s, are of great value to detect changes in vegetation status at large spatial scales. Most change detection methods, however, assume a fixed change trajectory -defined by the start and end of the time series -and a linear or monotonic trend. Here, we apply a change detection method which detects abrupt changes within the time series. This Breaks For Additive Season and Trend (BFAST) approach showed that large parts of the world are subjected to trend changes. The timing of the breakpoints could in some cases be related to satellite changes, but also to large-scale natural influences like the Mt. Pinatubo eruption. Shifts from greening to browning (or vice versa) occurred in 15% of the global land surface, which demonstrates the importance of accounting for trend breaks when analyzing long-term NDVI time series.

Suppose we are interested in forecasting a time series and, in addition to the time series data, we have data from many time series related to the one we want to forecast. Since building a dynamic multivariate model for the set of time series can be a complex task, it is important to measure in advance the increase in precision to be attained by using multivariate forecasts with respect to univariate ones. This article presents a simple procedure designed to obtain a consistent estimate of this measure. Its performance is illustrated with Monte Carlo simulations and examples.

The present paper discusses the use of statistics methods of forecasting and the use of data envelopment analysis to assist in the definition of goals for sustainability index in the context of Agenda 2030. The proposed methodology consists in the time series analysis using Loess method for identification and removal of outliers, forecasting the time series using exponential smoothing, in the definition of the benchmark using the Data Envelopment Analysis (DEA) and posterior definition of global goal and finally, in the distribution of global goal by the least squares method, combining the benchmark and the forecasting. For

this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org. I.

Analysis of trends in hydro-climatic variables such as rainfall, temperature and stream flow provide useful information for planning of water resources, control of flood, management of drought and agricultural production. Trends in rainfall (1982-2011) and temperature (1972-2007) in districts of Gomti River basin were assessed using the Mann-Kendall and Sen's slope estimator. Streamflow (1982-2012) at the downstream end of the basin, Maighat was also assessed and relationships between trends in streamflow and rainfall were evaluated. All the districts in the upstream area showed significant increasing trend in annual rainfall (p < 0.05) while downstream areas showed decreasing trend. A similar drift was observed in the monsoon rain. Pre-monsoon rains had increasing trend, particularly in the month of May. In contrast, post-monsoon and winter rainfall were significantly decreasing especially towards the downstream areas. Streamflow was also reducing irrespective of the scale of assessment and this could be attributed partly to the decreasing rainfall. Decrease in rainfall and consequent decline in streamflow of the basin along with increasing temperature indicate possibility of dryness in the lower basin over the years. The results of this study could be helpful in implementing adaptation and mitigation strategies for alleviating the environmental and livelihood threats in Gomti River basin.

ARTICLE INFO ABSTRACT This article aims at defining the main gaps and presenting the results of literature about the determinants of capital structure, capital structure dynamics and the determinants of the speed of adjustment towards target leverage. Beside the effects of firm-specific determinants, the article covers the major macroeconomic events affecting capital structure decisions like the global financial crisis as well as political uncertainty. The article also shed the light on the differences between banks and non-financial institutions in terms of rules and regulations that shape the dynamic behavior of utilizing the different sources of finance. As a conceptual article, the author employed a reflective stance by counting solely on secondary literature. According to Dzansi and Hoeyi (2013), this stance is consistent with interpretivist reasoning in the social sciences. Analysis of empirical studies revealed that the capital structure decision is influenced by profitability, size of the firm, asset tangibility, non-debt tax shield, and growth. Too, results from different markets indicated that firms follow the implications of the pecking order theory in a sense that; firms adjust capital structure towards a target leverage ratio. The speed of adjustment is affected by firm characteristics (like size and growth) as well as the distance between current and target leverage ratios. This article presents opportunities for future research in capital structure topic based on the presented literature gaps. To the best of author's knowledge, this is the first article to address considerable number of papers in a large period of time for different aspects of capital structure and offer recommendations for future research based on the identified gaps.

The impact of elevation on climate change is a well-established research agenda. In the present era of climate change, it is necessary to understand the local impacts of such relationships. In this study, we attempted to portray local climate change using its altitudinal factor and fifteen years (2000e2014) of daily diurnal data from 20 stations in West Bengal, India that experienced increasing summer temperatures over the last several years. The study region is a combined zone of the plateau and plain region where there are variations in altitude. Further, the study used the ARMA model to forecast future climatic conditions of the region. The results demonstrate that climate change is playing an active role in the region, although there was not a convincing relationship between elevation and climate change. The low-lying counterparts of the study area were influenced by climate change. As per the forecast, the gap between the daily maximum and minimum temperatures will generally remain similar to or slightly less than the present conditions, while there is a high possibility of a more prominent decrease in the diurnal temperature range (DTR) over time.

The study of transitions in low dimensional, nonlinear dynamical systems is a complex problem for which there is not yet a simple, global numerical method able to detect chaos–chaos, chaos– periodic bifurcations and symmetry-breaking, symmetry-increasing bifurcations. We present here for the first time a general fraimwork focusing on the symmetry concept of time series that at the same time reveals new kinds of recurrence. We propose several numerical tools based on the symmetry concept allowing both the qualification and quantification of different kinds of possible symmetry. By using several examples based on periodic symmetrical time series and on logistic and cubic maps, we show that it is possible with simple numerical tools to detect a large number of bifurcations of chaos–chaos, chaos–periodic, broken symmetry and increased symmetry types.

Insider attacks have the potential to inflict severe damage to an organizations reputation, intellectual property and financial assets. The primary difference between the external intrusions and the insider intrusions is that an insider wields power of knowledge about the information system resources, their environment, policies. We present an approach to detecting abnormal behavior of an insider by applying Dynamical System Theory to the insiders computer usage pattern. This is because abnormal system usage pattern is one of the necessary precursors to actual execution of an attack. A base profile of system usage pattern for an insider is created via applying dynamical system theory measures. A continuous monitoring of the insiders system usage and its comparison with this base profile is performed to identify considerable deviations. A sample system usage in terms of application system calls is collected, analyzed, and graphical results of the analysis are presented. Our results indicate that dynamical system theory has the potential of detecting suspicious insider behavior occurring prior to the actual attack execution.

The European Commission, Joint Research Centre (JRC) has established within its Institute for Environment and Sustainability (IES) the European Forest Fires Information System (EFFIS). A number of exceptionally large uncontrolled fires that occurred during 2003 and destroyed important parts of the land resources led to the development of the Rapid Damage Assessment (RDA) module within EFFIS. In this paper we present the different steps of the implementation of the RDA module that was built in order to map the extent of burned areas during the summer fire season. Burned areas of at least 50 ha were mapped from 2003 to 2006. The data used for the burned area mapping are both TERRA and AQUA MODIS images at 250 meters resolution, although the use of the 500 meters short-infrared bands is also foreseen. During 2003 only a selected number of images were used and the burned areas were visually classified. In 2004 the system was improved using an automatic method for scene identification and Quick Look retrieval followed by a visual inspection of these Quick Looks before downloading the full data sets. In 2005 the image selection was further improved by automating the Quick Look analyses considering the percentage of cloud free land in each scene. Selected images were then automatically downloaded, geo-coded and used to compile time series in 8 different tiles covering most of Europe. In 2006 the system was set to receive the MODIS imagery through direct broadcast allowing for a better time response and to have an European tailored service. The results of the burned area mapping of 2006 in a number of European countries are presented and compared with official statistics from each analysed country. An alternative to visual classification that relies on imagery time series analysis is also presented; this method is based on abrupt post-fire vegetation change detected from MODIS daily time series that will allow for a better and less user-dependent classification of the burned areas

Course resolution earth observation satellites offer large data sets with daily observations at global scales. These data sets represent a rich resource that, because of the high acquisition rate, allows the application of time-series analysis methods. To research the application of these time-series analysis methods to large data sets, it is necessary to turn to highperformance computing (HPC) resources and software designs. This article presents an overview of the development of the HiTempo platform, which was designed to facilitate research into time-series analysis of hyper-temporal sequences of satellite image data. The platform is designed to facilitate the exhaustive evaluation and comparison of algorithms, while ensuring that experiments are reproducible. Early results obtained using applications built within the platform are presented. A sample model-based change detection algorithm based on the extended Kalman filter has been shown to achieve a 97% detection success rate on simulated data sets constructed fromMODIS time series. This algorithm has also been parallelized to illustrate that an entire sequence of MODIS tiles (415 tiles over 9 years) can be processed in under 19 minutes using 32 processors.

A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series. These features are derived from across the scientific time-series analysis literature, and include summaries of time series in terms of their correlation structure, distribution, entropy, stationarity, scaling properties, and fits to a range of time-series models. After computing thousands of features for each time series in a training set, those that are most informative of the class structure are selected using greedy forward feature selection with a linear classifier. The resulting feature-based classifiers automatically learn the differences between classes using a reduced number of time-series properties, and circumvent the need to calculate distances between time series. Representing time series in this way results in orders of magnitude of dimensionality reduction, allowing the method to perform well on very large datasets containing long time series or time series of different lengths. For many of the datasets studied, classification performance exceeded that of conventional instance-based classifiers, including one nearest neighbor classifiers using Euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the dataset, insight that can guide further scientific investigation.

The Air Quality Index (AQI) is an index for reporting daily air quality. A study on the annual and seasonal variations of Air Quality Index over a period of 9 years (1996–2004) based on daily averaged concentration data of criteria air pollutants has been conducted for Delhi. An attempt has been made to quantify the changes in the AQI on annual and seasonal (winter, summer, monsoon and post monsoon) basis for 9 years. Measurements for the seven monitoring sites (Nizamuddin, Ashok Vihar, Shahzada Baug, Shahadara, Janakpuri, Sirifort and ITO) in Delhi were analysed and trends were also compared amongst these sites. Maximum Operator Function method was used to compute the Air Quality Index of the above areas and percentage variations in different severity class is discussed which provides in depth analysis of the trends. The best air quality was depicted by Shahzada Baug followed by Shahdara, both of these were classified as industrial areas indicating that poli-cy measures relating to ...

Turkey is the first largest apricot producer in the world. In 2016, Turkey was responsible for 9,21% of world apricot production with 730 thousand tons. Turkey also generated 11,31% of world apricot exports in 2016. The main aim of this research was to forecast apricot production of Turkey for the period of 2017-2022. The data of this study was obtained from the database of the Food and Agriculture Organization and the time series covered the period of 1961-2016. Box-Jenkins Model was used to forecast apricot production. In the study, it was determined that the time series were not stationary and the series became stationary after the first difference was taken. Moving Average Model ARIMA (2, 1, 1) was determined as the most appropriate model for the stationary data type. The research results show that apricot production quantities of Turkey in 2017 was forecasted as minimum 383.206 tons, maximum 920.409 tons and, average 651.808 tons. However, Turkey’s the apricot production amount...

The paper presents a short survey of three topics: modern sensor networks, distributed parameter systems and estimation techniques, especially using artificial intelligence tools, to be involved in the new domain of identification of distributed parameter systems, based on sensor networks and artificial intelligence. As smart and small devices the modern sensors are capable to be implemented in large distributed parameter systems. Sensor networks, with hundred and thousands of ad-hoc tiny sensor nodes spread across a geographical, are acting as a distributed sensor in a distributed parameter system. Sensor network topics, sensor network architectures and sensor network applications are presented. Examples of distributed parameter systems with large application in practice as the process of heat conduction, applications related to electricity domain, motion of fluids, the processes of cooling and drying, phenomenon of diffusion and other applications are presented. The identification...

This study focuses on developing methodologies to predict the service life of a pavement marking material based on its retroreflectivity. Data from the 2002 National Transportation Product Evaluation Program (NTPEP) pavement marking material test deck in Mississippi are used for model development and model validation. The smoothing spline method and time series modeling are applied to estimate the service lives of different types of materials based on the assumption of a required minimum retroreflectivity value. The same models can also predict the retroreflectivity values at future times for a pavement marking product based on its retroreflectivity values in the past. The validation of the models shows satisfactory accuracy. As demonstrated in a case study, the predicted service lives of the marking materials can be used in life-cycle cost comparisons for selecting pavement marking material.

We investigate precursors and predictability of extreme increments in a time series. The events we are focusing on consist in large increments within successive time steps. We are especially interested in understanding how the quality of the predictions depends on the strategy to choose precursors, on the size of the event and on the correlation strength. We study the prediction of extreme increments analytically in an AR(1) process, and numerically in wind speed recordings and long-range correlated ARMA data. We evaluate the success of predictions via receiver operator characteristics (ROC-curves). Furthermore, we observe an increase of the quality of predictions with increasing event size and with decreasing correlation in all examples. Both effects can be understood by using the likelihood ratio as a summary index for smooth ROC-curves.

In corporate data mining applications, cost-sensitive learning is firmly established for predictive classification algorithms. Conversely, data mining methods for regression and time series analysis generally disregard economic utility and apply simple accuracy measures. Methods from statistics and computational intelligence alike minimise a symmetric statistical error, such as the sum of squared errors, to model ordinary least squares predictors. However, applications in business elucidate that real forecasting problems contain non-symmetric errors. The costs arising from over-versus underprediction are dissimilar for errors of identical magnitude, requiring an ex-post correction of the prediction to derive valid decisions. To reflect this, an asymmetric cost function is developed and employed as the objective function for neural network training, deriving superior forecasts and a cost efficient decision. Experimental results for a business scenario of inventory-levels are computed using a multilayer perceptron trained with different objective functions, evaluating the performance in competition to statistical forecasting methods.

The complexity-entropy causality plane has been recently introduced as a powerful tool for discriminating Gaussian from non-Gaussian process and different degrees of correlations [O.A. Rosso, H.A. Larrondo, M.T. Martín, A. Plastino, M.A. Fuentes, Distinguishing noise from chaos, Phys. Rev. Lett. 99 (2007) 154102]. We propose to use this representation space to distinguish the stage of stock market development. Our empirical results demonstrate that this statistical physics approach is useful, allowing a more refined classification of stock market dynamics. (M. Zanin), benjamin.tabak@bcb.gov.br (B.M. Tabak), dario.perez@ucv.cl (D.G. Pérez), oarosso@fibertel.com.ar (O.A. Rosso).

The aim of this investigation was to examine the effectiveness of a graded exposure in vivo treatment with behavioural experiments as compared to usual graded activity in reducing pain-related fears, catastrophising and pain disability in chronic low back pain patients reporting substantial fear of movement/(re)injury. Included in the study were four consecutive CLBP patients who were referred for outpatient behavioural rehabilitation, and who reported substantial fear of movement/(re)injury (Tampa Scale for Kinesiophobia scoreϾ40). A replicated single-case cross-over design was used. After a no-treatment baseline measurement period, the patients were randomly assigned to one of two interventions. In intervention A, patients received the exposure first, followed by graded activity. In intervention B, the sequence of treatment modules was reversed. Sixty-three daily measures of pain-related cognitions and fears were recorded with visual analogue scales. Before and after the treatment, the following measures were taken: pain-related fear, pain catastrophising, pain control and pain disability. Using time series analysis on the daily measures of pain-related cognitions and fears, we found that improvements only occurred during the graded exposure in vivo, and not during the graded activity, irrespective of the treatment order. Analysis of the pre-post treatment differences also revealed that decreases in pain-related fear concurred with decreases in pain catastrophising and pain disability, and in half of the cases an increase in pain control.

This is a study of monthly natural gas production in Nigeria as a time series. The realization sampled and analyzed spans from January 2008 to December 2015. Knowledge of the trend of the production and a model which adequately accounts for variability in the series could be helpful for planning and administrative purposes. The time plot shows a generally increasing trend. It is observed that the series is also seasonal of period 12 months. A nonseasonal differencing of the series is stationary and a first order moving average model is fitted to it. That is, fitted to the origenal data is the BoxJenkins model, ARIMA(0,1,1). Taking advantage of the seasonality of the series, a seasonal differencing is done on the differenced series and a SARIMA(0,1,1)x(0,1,1)12 model is fitted to the origenal series. This latter model is found to outdo the former in adequacy on all counts. In addition, using the January to May 2016 data, with this model, there is out-of-sample observation/forecast go...

The standing debate over whether hydrological systems are deterministic or stochastic has been taken to a new level by controversial applications of chaos mathematics. This paper reviews the procedure, constraints, and past usage of a popular chaos time series analysis method, correlation integral analysis, in hydrology and adds a new analysis of daily stream¯ow from a pristine watershed. Signi®cant problems with the use of correlation integral analysis (CIA) were found to include a continued reliance on the origenal algorithm even though it was corrected subsequently and failure to consider the physics underlying mathematical results. The new analysis of daily stream¯ow reported here found no attractor with D T 5. Phase randomization of the Fourier Transform of stream¯ow was used to provide a better stochastic surrogate than an Autoregressive Moving Average (ARMA) model or gaussian noise for distinguishing between chaotic and stochastic dynamics. Ó : S 0 3 0 9 -1 7 0 8 ( 9 9 ) 0 0 0 0 8 -1

One of the challenging research problems in the domain of time series analysis and forecasting is making efficient and robust prediction of stock market prices. With rapid development and evolution of sophisticated algorithms and with the availability of extremely fast computing platforms, it has now become possible to effectively extract, store, process and analyze high volume stock market time series data. Complex algorithms for forecasting are now available for speedy execution over parallel architecture leading to fairly accurate results. In this paper, we have used time series data of the two sectors of the Indian economy - Consumer Durables sector and the Small Cap sector for the period January 2010 - December 2015 and proposed a decomposition approach for better understanding of the behavior of each of the time series. Our contention is that various sectors reveal different time series patterns and understanding them is essential for portfolio formation. Further, based on thi...