Technique For Time Series Data Research Articles

Genetic protein sequence analysis based on sequence alignment techniques for time series data

This study aims to explore the application and effectiveness of sequence comparison techniques in dealing with missing and outliers in time series data. First, the data are pre-processed by convolutional neural network (CNN) and recurrent neural networks (RNN) to remove noise and outliers. Then, time series data at different time points are compared and analysed using the comparison loss function to identify changes and differences in the data. Finally, the prediction performance of different models is evaluated using a variety of assessment metrics, and the results are compared and analysed to verify the effectiveness of the sequence comparison technique in dealing with missing and outliers. The experimental results show that the sequence comparison technique can effectively deal with missing and outliers in time series data, providing important insights for further research on the application and development of the sequence comparison technique. Future research can explore the application of sequence comparison techniques in more fields to optimize model performance and improve accuracy and stability.

Time series analysis and forecasting of cholera disease using discrete wavelet transform and seasonal autoregressive integrated moving average model

PurposeCholera is among the leading causes of death in Nigeria. The main predictors of cholera transmission remain the lack of access to potable water and good sanitary conditions. Cholera is also linked to weather variables such as maximum temperatures, high Rainfall, and humidity. The relationship between cholera cases and weather variables depends on location, time, or season; hence, it is a time series dataset. This research aims to enhance the seasonal autoregressive integrated moving average (SARIMA) model by incorporating the discrete wavelet transform (DWT). MethodsThis research proposed a novel approach to forecasting cholera using the SARIMA model by incorporating DWT as a dimensionality reduction technique and a K-means clustering algorithm for outlier detection. The enhanced model is termed the "Enhanced seasonal autoregressive integrated moving average" (ESARIMA). DWT is a good dimensionality reduction technique for time series data and extracts the best features for forecasting to have better prediction accuracy and minimal error. ResultThe results show that ESARIMA (accuracy = 97%, RSS = 0.502) outperformed the existing model, SARIMA (accuracy = 91.61%, RSS = 0.60). ConclusionNigeria's weekly and monthly cholera outbreaks exhibit stochastic seasonal time series behavior that becomes stationary after the first seasonal differencing; hence, it could be forecasted with specific time series models.

Analyzing IoT Attack Feature Association with Threat Actors

Internet of Things (IoT) refers to the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data. These devices can be controlled remotely, which makes them susceptible to exploitation or even takeover by an attacker. The lack of security features on many IoT devices makes them easy to access confidential information, issue commands from a distance, or even use the compromised device as part of a DDoS attack against another network. Feature selection is an important part of problem formulation in machine learning. To overcome the above problems, this paper proposes a novel feature selection framework RFS for IoT attack detection using machine learning (ML) techniques. The RFS is based on the concept of effective feature selection and consists of three main stages: feature selection, modeling, and attacks detection. For feature selection, three different models are proposed. Based on these approaches, three different algorithms are proposed. A set of 40 features was included in the model, derived from combinatorial optimization and statistical analysis methods. Our experimental study shows that the proposed frame work significantly improves over state-of-the-art cyberattacks techniques for time series data with outliers.

A comparison of single and multiple changepoint techniques for time series data

Correlated time series data arise in many applications. This paper describes and compares several prominent single and multiple changepoint techniques for correlated time series. In the single changepoint problem, various cumulative sum (CUSUM) and likelihood ratio statistics, along with boundary cropping scenarios and scaling methods (e.g., scaling to an extreme value or Brownian Bridge limit) are compared. A recently developed test based on summing squared CUSUM statistics over all time indices is shown to have controlled Type I error and superior detection power. In the multiple changepoint setting, penalized likelihoods drive the discourse, with AIC, BIC, mBIC, and MDL penalties being considered. Binary and wild binary segmentation techniques are also compared. A new distance metric is introduced that measures differences between two multiple changepoint segmentations. Algorithmic and computational concerns are discussed and simulations are given to support all conclusions. In the end, the multiple changepoint setting admits no clear methodological winner, performance depending on the particular scenario. Nonetheless, some practical guidance emerges.

Natural resources and economic growth: evidence from Brunei Darussalam

PurposeThis paper explores the relationship between natural resources and economic growth of Brunei Darussalam, an underresearched area in the available literature.Design/methodology/approachAnnual data are sourced from reliable sources for the period 1989–2020. Appropriate cointegration techniques for time series data are employed to estimate the specified models and extract results.FindingsThe results provide evidence about the positive and significant role that natural resources have played in the economic growth of Brunei Darussalam. Similarly, trade openness and domestic investment have also positively and significantly impacted the long-run economic growth. On the other hand, the impacts of government expenditure and the growth of human capital on economic growth are although positive but insignificant statistically in the long run. The short-run results show that natural resources, government expenditures and domestic investment have influenced economic growth both positively and significantly. Moreover, the positive and significant impact of trade openness on economic growth, which was observed in the long run, turned negative and insignificant in the short run. Finally, the insignificant positive relationship between the growth of human capital and economic growth observed in the long run remained the same in the short run.Originality/valueThis paper studies the resource curse hypothesis for Brunei Darussalam for the first time, and therefore, the findings will be of significant interest for policymakers and researchers.

Greenhouse gas emissions, non-renewable energy consumption, and output in South America: the role of the productive structure.

Most of the countries of South America depend heavily on mining and agriculture, which develops through the destruction of the forest. The expansion of the agricultural frontier is more visible in countries with proximity to the Amazon. Otherwise, the increase in urban primacy and real income per capita has led to an increase in the use of non-renewable energy in recent decades. The interest in quantifying greenhouse gas emissions has increased, oriented towards the search for mechanisms that mitigate the irreversible effects of climate change. In this context, the objective of this research is to examine the causal link among non-renewable energy consumption and real GDP per capita in greenhouse gas emissions in ten countries of South America during 1971-2014. In addition, we group the countries according to their productive structure and we incorporate the structural changes of each country in the econometric estimations, allowing to significantly improve the understanding of the sources of greenhouse gases. We use cointegration and causality techniques for time series data, and we found that there is a relationship of short- and long-term equilibrium between the three variables in all countries. The causality test indicates that in Bolivia, Peru, and Uruguay, there is causality from the consumption of non-renewable energy to greenhouse gas emissions. Likewise, in Venezuela and the agricultural countries, GDP causes greenhouse gas emissions. An implication of the public policy derived from this research is that most of the countries of South America can promote a change in the energy matrix to contribute to the mitigation of greenhouse gas emissions without limiting economic growth.

Fusion Process Neural Networks Classifier Oriented Time Series

Based on the consideration of complementary advantages, different wavelet, fractal and statistical methods are integrated to complete the classification feature extraction of time series. Combined with the advantage of process neural networks that processing time-varying information, we propose a fusion classifier with process neural network oriented time series. Be taking advantage of the multi-fractal processing nonlinear feature of time series data classification, the strong adaptability of the wavelet technique for time series data and the effect of statistical features on the classification of time series data, we can achieve the classification feature extraction of time series. Additionally, using time-varying input characteristics of process neural networks, the pattern matching of timevarying input information and space-time aggregation operation is realized. The feature extraction of time series with the above three methods is fused to the distance calculation between time-varying inputs and cluster space in process neural networks. We provide the process neural network fusion to the learning algorithm and optimize the calculation process of the time series classifier. Finally, we report the performance of our classification method using Synthetic Control Charts data from the UCI dataset and illustrate the advantage and validity of the proposed method.

Alignment of time-resolved data from high throughput experiments.

To better understand the dynamics of the underlying processes in cells, it is necessary to take measurements over a time course. Modern high-throughput technologies are often used for this purpose to measure the behavior of cell products like metabolites, peptides, proteins, [Formula: see text]RNA or mRNA at different points in time. Compared to classical time series, the number of time points is usually very limited and the measurements are taken at irregular time intervals. The main reasons for this are the costs of the experiments and the fact that the dynamic behavior usually shows a strong reaction and fast changes shortly after a stimulus and then slowly converges to a certain stable state. Another reason might simply be missing values. It is common to repeat the experiments and to have replicates in order to carry out a more reliable analysis. The ideal assumptions that the initial stimulus really started exactly at the same time for all replicates and that the replicates are perfectly synchronized are seldom satisfied. Therefore, there is a need to first adjust or align the time-resolved data before further analysis is carried out. Dynamic time warping (DTW) is considered as one of the common alignment techniques for time series data with equidistant time points. In this paper, we modified the DTW algorithm so that it can align sequences with measurements at different, non-equidistant time points with large gaps in between. This type of data is usually known as time-resolved data characterized by irregular time intervals between measurements as well as non-identical time points for different replicates. This new algorithm can be easily used to align time-resolved data from high-throughput experiments and to come across existing problems such as time scarcity and existing noise in the measurements. We propose a modified method of DTW to adapt requirements imposed by time-resolved data by use of monotone cubic interpolation splines. Our presented approach provides a nonlinear alignment of two sequences that neither need to have equi-distant time points nor measurements at identical time points. The proposed method is evaluated with artificial as well as real data. The software is available as an R package tra (Time-Resolved data Alignment) which is freely available at: http://public.ostfalia.de/klawonn/tra.zip .

LONG TERM WIND SPEED PREDICTION USING WAVELET COEFFICIENTS AND SOFT COMPUTING

In the past researches, scholars have carried out short-term prediction for wind speed. The present work deals with long-term wind speed prediction, required for hybrid power generation design and contract planning. As the total database is quite large for long-term prediction, feature extraction of data by application of Lifting wavelet coefficients are exploited, along with soft computing techniques for time series data, which is scholastic in nature.

Improvement of Japanese Signature Verification by Combined Segmentation-verification Approach

This paper proposes a new signature verification technique called combined segmentation-verification based on off-line features and on-line features. We use three different off-line feature vectors extracted from full name Japanese signature image and from the sub-images of the first name and the last name. The Mahalanobis distance for each offline feature vector is calculated for signature verification. The on-line feature based technique employs dynamic programming (DP) matching technique for time series data of the signatures. The final decision (verification) is performed by SVM based on the three Mahalanobis distances and the dissimilarity of the DP matching. In the evaluation test the proposed technique achieved 97.22% verification accuracy with even FRR and FAR, which is 3.95% higher than the best accuracy obtained by the individual technique. This result shows that the proposed combined segmentation verification approach improves Japanese signature verification accuracy significantly.

Preface to Special Issue on Analysis Technique for Time Series Data in Power System

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Analysis and Forecasting Techniques for Time-series Data of Demand and Supply Control in Power Systems

Analysis and Forecasting Techniques for Time-series Data of Demand and Supply Control in Power Systems

Examining deterrence of adult sex crimes: A semi-parametric intervention time-series approach

Motivated by recent developments on dimension reduction (DR) techniques for time-series data, the association of a general deterrent effect with the registration and notification (SORN) policy of South Carolina (SC) for preventing sex crimes was examined. Using adult sex crime arrestee data from 1990 to 2005, the idea of a central mean subspace (CMS) is extended to intervention time-series analysis (CMS-ITS) to model the sequential intervention effects of 1995 (the year SC’s SORN policy was initially implemented) and 1999 (the year the policy was revised to include online notification) on the time-series spectrum. The CMS-ITS model estimation was achieved via kernel smoothing techniques, and compared to interrupted auto-regressive integrated time-series (ARIMA) models. Simulation studies and application to the real data underscore our model’s ability of achieving parsimony, and of detecting intervention effects not earlier determined via traditional ARIMA models. From a public health perspective, findings from this study draw attention to the potential general deterrent effects of SC’s SORN policy. These findings are considered in light of the overall body of research on sex crime arrestee registration and notification policies, which remain controversial.

Seasonal clustering technique for time series data

In data mining, the unsupervised learning technique of clustering is a useful method for ascertaining trends and patterns in data. Most general clustering techniques do not take into consideration the time-order of data. In this paper, mathematical programming and statistical techniques and methodologies are combined to develop a seasonal clustering technique for determining clusters of time series data. We apply this technique to weather and aviation data to determine probabilistic distributions of arrival capacity scenarios, which can be used for efficient traffic flow management. In general, this technique may be used for seasonal forecasting and planning.

Maximum Entropy Spectra and the Spatial and Temporal Dimensions of Economic Fluctuations in an Urban System

Geographical AnalysisVolume 13, Issue 2 p. 95-116 Free Access Maximum Entropy Spectra and the Spatial and Temporal Dimensions of Economic Fluctuations in an Urban System Claude Marchand, Claude Marchand The cartographic work of Jennifer Wilcox, University of Toronto, is gratefully acknowledged. Claude Marchand is assistant professor of geography, University of Montreal.Search for more papers by this author Claude Marchand, Claude Marchand The cartographic work of Jennifer Wilcox, University of Toronto, is gratefully acknowledged. Claude Marchand is assistant professor of geography, University of Montreal.Search for more papers by this author First published: April 1981 https://doi.org/10.1111/j.1538-4632.1981.tb00719.xCitations: 17 Claude Marchand is assistant professor of geography, University of Montreal. The cartographic work of Jennifer Wilcox, University of Toronto, is gratefully acknowledged. AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat LITERATURE CITED Bartels, C. P. A. (1977). “The structure of Regional Unemployment in the Netherlands. Regional Science and Urban Economics, 7, 103– 5. Bartels, C. P. A., M. Booleman, and W. H. Peters (1978). “A Statistical Analysis of Regional-Unemployment Series. Environment and Planning A, 10, 937– 54. Bennett, R. J. (1979). Spatial Time Series: Analysis—Forecasting—Control. London: Pion. Brechling, F. (1967). “Trends and Cycles in British Regional Unemployment. 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