Application Of Time Series Analysis Research Articles

The cost and cost trajectory of whole-genome analysis guiding treatment of patients with advanced cancers.

BackgroundLimited data exist on the real‐world costs of applying whole‐genome analysis (WGA) in a clinical setting. We estimated the costs of applying WGA to guide treatments for patients with advanced cancers and characterized how costs evolve over time.MethodsThe setting is the British Columbia Cancer Agency Personalized OncoGenomics (POG) program in British Columbia, Canada. Cost data were obtained for patients who enrolled in the program from 2012 to 2015. We estimated mean WGA costs using bootstrapping. We applied time series analysis and produced 10‐year forecasts to determine when costs are expected to reach critical thresholds.ResultsThe mean cost of WGA over the study period was CDN$34,886 per patient (95% CI: $34,051, $35,721). Over time, WGA costs decreased, driven by a reduction in costs of sequencing. Yet, costs of other components of WGA increased. Forecasting showed WGA costs may not reach critical thresholds within the next 10 years.Conclusion WGA costs decreased over the studied time horizon, but expenditures needed to realize WGA remain significant. Future research exploring costs and benefits of WGA‐guided cancer care are crucial to guide health policy.

Research on Time-series Modeling and Filtering Methods for MEMS Gyroscope Random Drift Error

The precision of MEMS gyroscope is reduced by random drift error. This paper applied time series analysis to model random drift error of MEMS gyroscope. Based on the model established, Kalman filter was employed to compensate for the error. To overcome the disadvantages of conventional Kalman filter, Sage-Husa adaptive filtering algorithm was utilized to improve the accuracy of filtering results and the orthogonal property of innovation in the process of filtering was utilized to deal with outliers. The results showed that, compared with conventional Kalman filter, the modified filter can not only enhance filter accuracy, but also resist to outliers and this assured the stability of filtering thus improving the performance of gyroscopes.

Forecasting Temporal and Spatial Climatological Influence for Land Suitability Evaluation in Bentota Sri Lanka

Climate change has raised much concern regarding its impacts on future land use planning, varying by region, time, and socio-economic development path. The principle purpose of land suitability evaluation is to predict the potential and limitation of the land for crop production and other land uses. This study was carried out to predict the temperature and rainfall trends as one of the major factor for evaluating land suitability. Climatic data such as monthly mean temperature, total monthly rainfall, maximum daily rainfall and total annual rainfall during last 30 years of all weather stations located in Bentota River basin was collected and analyzed applying time series analysis, correlation analysis and Manna Kendall trend test methods. Spatial distribution of forecast rainfall values was illustrated applying Arc GIS software. The findings revealed that monthly mean temperature and maximum daily rainfall had a general increasing trend whereas, total monthly rainfall and total annual rainfall showed a general decreasing trend in Bentota area. It was indicated relatively high rainfall situations during May and October while low rainfall situations during January and February by occurring flood situation in once per five year. During Yala season the area will be received comparatively more rainfall (331mm) than Maha season (300mm) in future. Community and the farmers in this area can be aware about the anticipated spatial distribution of total monthly rainfall during two major seasons and flood occurrence periods. Decision makers should evaluate land suitability of Bentota area by considering above climatological influences and its spatial distribution pattern that identified as major outcome of this research. The approach and the methodology adopted in this study will be useful for other researchers, agriculturalist and planners to identify the future climatological influences and its spatial distribution pattern for land suitability evaluations and other decision making purposes for other areas.

Collaborative Research with Chinese, Indian, Filipino and North European Research Organizations on Infectious Disease Epidemics.

In this report, we present a short review of applications of time series analysis, which consists of spectral analysis based on the maximum entropy method in the frequency domain and the least squares method in the time domain, to the incidence data of infectious diseases. This report consists of three parts. First, we present our results obtained by collaborative research on infectious disease epidemics with Chinese, Indian, Filipino and North European research organizations. Second, we present the results obtained with the Japanese infectious disease surveillance data and the time series numerically generated from a mathematical model, called the susceptible/exposed/infectious/recovered (SEIR) model. Third, we present an application of the time series analysis to pathologic tissues to examine the usefulness of time series analysis for investigating the spatial pattern of pathologic tissue. It is anticipated that time series analysis will become a useful tool for investigating not only infectious disease surveillance data but also immunological and genetic tests.

Application of Time Series Analyses in Big Data: Practical, Research, and Education Implications

The application of Big Data and time series models is currently at an early stage. This paper examines the relevance and use of time series analyses for Big Data and business analytics by discussing the emergence of Big Data in business, presenting time series models, and providing an example of how time series models can be efficiently and effectively applied in accounting and auditing using Big Data. Using sophisticated Big Data and time series models, millions of transactions can be searched to spot patterns and detect abnormalities and irregularities. The time series model and Big Data analysis presented in this paper provide policy, practical, educational, and research implications. Businesses and management can use our suggested time series model and Big Data analysis in their predictive models of managerial strategies, decisions, and actions. Business schools and accounting programs can integrate the time series model, Big Data and data analytics into business and accounting education.

A new variance component score test for testing distributed lag functions with applications in time series analysis

We propose to test a given constrained distributed lag model (DLM) of the form β=Cθ against an unconstrained alternative using a variance component score test (VCST) and show that VCST is more powerful than the standard likelihood ratio test in a simulation study.

Application of Time-Series Analysis for Predicting Defects in Continuous Steel Casting Process

Abstract The purpose of this paper was testing suitability of the time-series analysis for quality control of the continuous steel casting process in production conditions. The analysis was carried out on industrial data collected in one of Polish steel plants. The production data concerned defective fractions of billets obtained in the process. The procedure of the industrial data preparation is presented. The computations for the time-series analysis were carried out in two ways, both using the authors’ own software. The first one, applied to the real numbers type of the data has a wide range of capabilities, including not only prediction of the future values but also detection of important periodicity in data. In the second approach the data were assumed in a binary (categorical) form, i.e. the every heat(melt) was labeled as ‘Good’ or ‘Defective’. The naïve Bayesian classifier was used for predicting the successive values. The most interesting results of the analysis include good prediction accuracies obtained by both methodologies, the crucial influence of the last preceding point on the predicted result for the real data time-series analysis as well as obtaining an information about the type of misclassification for binary data. The possibility of prediction of the future values can be used by engineering or operational staff with an expert knowledge to decrease fraction of defective products by taking appropriate action when the forthcoming period is identified as critical.

Hourly Average Wind Speed Simulation and Forecast Based on ARMA Model in Jeju Island, Korea

This paper presents an application of time series analysis in hourly wind speed simulation and forecast in Jeju Island, Korea. Autoregressive – moving average (ARMA) model, which is well in description of random data characteristics, is used to analyze historical wind speed data (from year of 2010 to 2012). The ARMA model requires stationary variables of data is satisfied by power law transformation and standardization. In this study, the autocorrelation analysis, Bayesian information criterion and general least squares algorithm is implemented to identify and estimate parameters of wind speed model. The ARMA (2,1) models, fitted to the wind speed data, simulate reference year and forecast hourly wind speed in Jeju Island.

Asymptotic normality of quadratic forms of martingale differences

We establish the asymptotic normality of a quadratic form Q_n in martingale difference random variables eta _t when the weight matrix A of the quadratic form has an asymptotically vanishing diagonal. Such a result has numerous potential applications in time series analysis. While for i.i.d. random variables eta _t, asymptotic normality holds under condition ||A||_{sp}=o(||A||) , where ||A||_{sp} and ||A|| are the spectral and Euclidean norms of the matrix A, respectively, finding corresponding sufficient conditions in the case of martingale differences eta _t has been an important open problem. We provide such sufficient conditions in this paper.

Measuring the Distance between Sets of ARMA Models

A distance between pairs of sets of autoregressive moving average (ARMA) processes is proposed. Its main properties are discussed. The paper also shows how the proposed distance finds application in time series analysis. In particular it can be used to evaluate the distance between portfolios of ARMA models or the distance between vector autoregressive (VAR) models.

How useful is the yield spread as a predictor of growth in Australia?

Purpose– The purpose of this paper is to examine the usefulness of the yield spread for forecasting growth in the Australian economy since 1969.Design/methodology/approach– This paper applies time series analysis to evaluate the in-sample and out-of-sample forecasting power of the spread-growth nexus in Australia for the period spanning from 1969 to 2014.Findings– This paper concludes that the spread serves as a useful predictor of growth in output, private dwellings, private fixed capital formation, and inventories in Australia, both in-sample and out-of-sample. Its predictive content is not sensitive to the inclusion of monetary policy variables or the switch to the inflation-targeting regime by the Reserve Bank of Australia in the early 1990s.Originality/value– This paper provides significant evidence to policy makers and market participants on the usefulness of the spread in forecasting output growth for up to eight quarters ahead.

Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China

The landslide displacement in the Three Gorges Reservoir, China, experiences step-like deformation that is influenced by rainfall and the periodic scheduling of the reservoir. In view of the step-like characteristic, the Particle Swarm Optimization and Support Vector Machine (PSO–SVM) coupling model based on the response of the induced factors was proposed to predict the landslide displacement. The moving average method was adopted to divide the total displacement into trend term and periodic term. The trend displacement was controlled by the geological conditions and predicted by polynomial function, while the periodic displacement was under the combined control of the triggers and the evolution state of the landslide. Therefore, the PSO–SVM model, based on the factors of the precipitation, the variation range of the reservoir and the displacements of the prior-periods, was proposed to predict the periodic displacement. The typical step-like landslide in the Three Gorges Reservoir, which is known as the Bazimen landslide, was taken as a case study to verify the prediction results. The values of the root mean square error and the mean absolute percentage error were 13.28 and 25.95, respectively. The results showed that rainfall and reservoir water level were the dominant factors for the step-like landslide deformation. The evolution state of the landslide was also significant in reflecting the response relationship between the displacement and inducing factors. In conclusion, the proposed PSO–SVM model can better represent the response relationship between the factors and the periodic displacement, which made the predicted values of the total displacement fit with the measured values greatly.

A Comparative Study on Application of Time Series Analysis for Traffic Forecasting in India: Prospects and Limitations

Modelling of growth trend and improvement in forecasting techniques for vehicular population has always been and will continue to be of paramount importance for any major infrastructure development initiatives in the transportation engineering sector. Although many traditional as well as some advanced methods are in vogue for this process of estimation, there has been a continuous quest for improving on the accuracy of different methods. Time-series (TS) analysis technique has been in use for short-term forecasting in the fields of finance and economics, and has been investigated here for its prospective use in traffic engineering. Towards this end, results obtained from two other traditional approaches, namely trend line analysis and econometric analysis, have also been collated, underlining the better results obtained from TS analysis. A regression model has been developed for predicting fatality rate and its results have been compared with those from TS analysis. Based on the incentive provided by reduced errors obtained from using increasing number of data points for model-building, forecasting has been done for the year 2021 using time-series modelling. With most of the datasets used and locations analysed for forecasting, the TS analysis technique has been found to be a useful tool for prediction, resulting in lower estimation errors for almost all the cases considered. It has also been inferred that the proximity of the forecasting window to the sample dataset has a noticeable effect on the accuracy of time-series forecasting, in addition to the amount of data used for analysis.

Global Gap-Free MERIS LAI Time Series (2002–2012)

This article describes the principles used to generate global gap-free Leaf Area Index (LAI) time series from 2002–2012, based on MERIS (MEdium Resolution Imaging Spectrometer) full-resolution Level1B data. It is produced as a series of 10-day composites in geographic projection at 300-m spatial resolution. The processing chain comprises geometric correction, radiometric correction, pixel identification, LAI calculation with the BEAM (Basic ERS & Envisat (A)ATSR and MERIS Toolbox) MERIS vegetation processor, re-projection to a global grid and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing, we applied time series analysis to fill data gaps and to filter outliers using the technique of harmonic analysis (HA) in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (<10°), topography and intermittent data reception. We applied our technique for the whole period of observation (July 2002–March 2012). Validation, carried out with VALERI (Validation of Land European Remote Sensing Instruments) and BigFoot data, revealed a high degree (R2 : 0.88) of agreement on a global scale.

Local linear smoothing for sparse high dimensional varying coefficient models

Varying coefficient models are useful generalizations of parametric linear models. They allow for parameters that depend on a covariate or that develop in time. They have a wide range of applications in time series analysis and regression. In time series analysis they have turned out to be a powerful approach to infer on behavioral and structural changes over time. In this paper, we are concerned with high dimensional varying coefficient models including the time varying coefficient model. Most studies in high dimensional nonparametric models treat penalization of series estimators. On the other side, kernel smoothing is a well established, well understood and successful approach in nonparametric estimation, in particular in the time varying coefficient model. But not much has been done for kernel smoothing in high-dimensional models. In this paper we will close this gap and we develop a penalized kernel smoothing approach for sparse high-dimensional models. The proposed estimators make use of a novel penalization scheme working with kernel smoothing. We establish a general and systematic theoretical analysis in high dimensions. This complements recent alternative approaches that are based on basis approximations and that allow more direct arguments to carry over insights from high-dimensional linear models. Furthermore, we develop theory not only for regression with independent observations but also for local stationary time series in high-dimensional sparse varying coefficient models. The development of theory for local stationary processes in a high-dimensional setting creates technical challenges. We also address issues of numerical implementation and of data adaptive selection of tuning parameters for penalization.The finite sample performance of the proposed methods is studied by simulations and it is illustrated by an empirical analysis of NASDAQ composite index data.

Applications of time-series analysis to mood fluctuations in bipolar disorder to promote treatment innovation: a case series.

Treatment innovation for bipolar disorder has been hampered by a lack of techniques to capture a hallmark symptom: ongoing mood instability. Mood swings persist during remission from acute mood episodes and impair daily functioning. The last significant treatment advance remains Lithium (in the 1970s), which aids only the minority of patients. There is no accepted way to establish proof of concept for a new mood-stabilizing treatment. We suggest that combining insights from mood measurement with applied mathematics may provide a step change: repeated daily mood measurement (depression) over a short time frame (1 month) can create individual bipolar mood instability profiles. A time-series approach allows comparison of mood instability pre- and post-treatment. We test a new imagery-focused cognitive therapy treatment approach (MAPP; Mood Action Psychology Programme) targeting a driver of mood instability, and apply these measurement methods in a non-concurrent multiple baseline design case series of 14 patients with bipolar disorder. Weekly mood monitoring and treatment target data improved for the whole sample combined. Time-series analyses of daily mood data, sampled remotely (mobile phone/Internet) for 28 days pre- and post-treatment, demonstrated improvements in individuals' mood stability for 11 of 14 patients. Thus the findings offer preliminary support for a new imagery-focused treatment approach. They also indicate a step in treatment innovation without the requirement for trials in illness episodes or relapse prevention. Importantly, daily measurement offers a description of mood instability at the individual patient level in a clinically meaningful time frame. This costly, chronic and disabling mental illness demands innovation in both treatment approaches (whether pharmacological or psychological) and measurement tool: this work indicates that daily measurements can be used to detect improvement in individual mood stability for treatment innovation (MAPP).

Spatial analysis of riparian forest soil macrofauna and its relation to abiotic soil properties

Soil macrofauna play major roles in ecosystem functioning; however the ecological effects of macrofauna are influenced by their spatial distribution. Close relationships to soil properties are one major factor for determining spatial pattern of macrofauna. Information about macrofauna distribution pattern is scarce especially at regional scales. The limited numbers of studies available suggest a considerable influence of soil properties on macrofauna distribution. Therefore, this study was conducted in riparian forest to elucidate spatial patterns of soil macrofauna and their relationship to abiotic soil properties at regional scale. Soil macrofauna abundance, diversity, richness and evenness were analyzed at 200 sampling points along parallel transects which ran perpendicular to a river. The hierarchical sampling design comprised maximum distances of 0.5km and minimum distances of 1m between samples. Soil macrofauna was extracted from 50cm×50cm×25cm soil monoliths by hand-sorting. At each transect point additional soil samples were taken for analysis of soil texture, standard soil properties and electrolytic conductivity. Data were analyzed using geostatistics (variograms and cross-variograms) in order to describe and quantify the spatial continuity of macrofauna characteristics and their relation to soil properties. The variograms revealed the presence of spatial autocorrelation in the majority of parameters. Also, relationships between macrofauna and soil properties such as soil texture and electrolytic conductivity, could be detected. To get more information about macrofauna distribution patterns and macrofauna–soil-relationships, subsets of the complete dataset were analyzed by means of applied time series analysis. The basic pattern of diversity value along the spatial series could be estimated by an autoregressive model. In addition, state-space analysis revealed that soil texture (silt) was important for estimating soil macrofauna diversity along transects. The study shows that geostatistical analyses as well as applied time series are suitable methods for analyzing macrofauna characteristics and for detecting relationships to soil properties. In contrast to geostatistics, state-space analysis yields additional information about the relative importance of different parameters for estimating macrofauna characteristics.

Simultaneous application of time series analysis and wavelet transform for determining surface roughness of the ground workpieces

The absence of a rapid method for the estimation of surface roughness has been considered as one of the main problems in accurate machining processes. The probability of encountering measurement equipment to other production equipments and unsuitable environments for the measurement equipment can be considered as the main problem for lack of utilizing them on the production. On the other hand, the surface roughness is not uniform at different regions of the manufactured workpiece. The vibration of the cutting tool and boundary conditions of the workpiece can be considered as the main source of these variations. Thus, in contact measurement method, the surface roughness must be determined by repeating and averaging of sampling results. In this paper, a novel non-contact method based on combination of the time series analysis and decomposition of the image by wavelet transform is presented. In this method, the image entropy criterion is used for specifying the appropriate position of the camera and light source relation to the calibrated surface roughness sample. Then, the surface texture of the captured image is extracted by wavelet transform. After that, the analysis of the images for finding the logical relationship between surface roughness and the largest Lyapunov exponent of the approximation coefficients is done. To confirm the suggested method, the grinding experiments on ck45 material with different cutting conditions are done, and their surface roughness has been specified and compared using the contact and the suggested method. The results indicate that the accuracy of the suggested method for determining the surface roughness of ground surface is significant.

O3-5-4 - Establishment of a terminal prognosis prediction model by applying time series analysis to real-world data

O3-5-4 - Establishment of a terminal prognosis prediction model by applying time series analysis to real-world data

Malthusian checks in pre-industrial Sweden and Finland: a comparative analysis of the demographic regimes

In this article, the existence of the Malthusian preventive and positive checks in pre-industrial Sweden and Finland are studied using demographic and economic data from circa 1750–1860. By applying time series analysis, we are able to identify strong preventive and positive checks for Sweden. The preventive check is considered to work both directly through births and indirectly through marriages. Although the Finnish data also indicate the existence of the preventive check, the positive check is only detected with differenced data. Our findings contradict the initial hypothesis that, due to poverty, Finland would display a higher sensitivity of mortality to living standards than Sweden. Instead, the finding of a pronounced marriage-driven preventive check in Finland casts new light on the macro-level determinants of the check mechanisms and on their connectivity to wider societal conditions.