Predicting Winter Flounder (Pseudopleuronectes americanus) Catches by Time Series Analysis

1989 ◽  
Vol 46 (4) ◽  
pp. 650-659 ◽  
Author(s):  
Perry Jeffries ◽  
Aimee Keller ◽  
Stephen Hale
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
New England ◽  
Rhode Island ◽  
Time Series Data ◽  
Maximum Error ◽  
Winter Flounder ◽  
Series Data ◽  
Pseudopleuronectes Americanus ◽  
Series Analysis

Long-term changes in catches of winter flounder (Pseudopleuronectes americanus) were compared at five locations within Narragansett Bay, Rhode Island, USA and in three areas directly offshore. In the lower Bay, relative abundance decreased 86% during warming in the early 1970's, recovered briefly, and then declined to an all-time low in 1986. Time-series analysis successfully predicted a recovery in 1987. Year ahead predictions, based on annual models, had a maximum error of 14% (1986); monthly forecasts explained 62–72% of catch variation during the entire investigation; cumulative monthly error (forecast compared with actual over an annual cycle) was 11% in the Bay and 21% offshore. Commercial offshore catch and the fish retained on power plant intake screens in two major rivers showed the same 11-yr pattern of population fall and recovery detected in the lower Bay. Coherent behavior was also demonstrated by the similarity of random processes which generated the time-series data. Thus a single time-series model for southern New England winter flounder seems feasible.

Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Analysis ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Time Series Data ◽  
Fuzzy Logic System ◽  
Series Data ◽  
Series Analysis ◽  
Logic System

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis

scholarly journals Assessment of Online Deliberative Quality: New Indicators Using Network Analysis and Time-Series Analysis

2021 ◽  
Vol 13 (3) ◽  
pp. 1187
Author(s):  
Bokyong Shin ◽  
Mikko Rask
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Collective Intelligence ◽  
Series Data ◽  
Online Data ◽  
Series Analysis ◽  
Study Results ◽  
Online Deliberation

Online deliberation research has recently developed automated indicators to assess the deliberative quality of much user-generated online data. While most previous studies have developed indicators based on content analysis and network analysis, time-series data and associated methods have been studied less thoroughly. This article contributes to the literature by proposing indicators based on a combination of network analysis and time-series analysis, arguing that it will help monitor how online deliberation evolves. Based on Habermasian deliberative criteria, we develop six throughput indicators and demonstrate their applications in the OmaStadi participatory budgeting project in Helsinki, Finland. The study results show that these indicators consist of intuitive figures and visualizations that will facilitate collective intelligence on ongoing processes and ways to solve problems promptly.

Exploring the dynamic contribution behavior of editors in wikis based on time series analysis

2016 ◽  
Vol 50 (1) ◽  
pp. 41-57 ◽  
Author(s):  
Linghe Huang ◽  
Qinghua Zhu ◽  
Jia Tina Du ◽  
Baozhen Lee
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Time Of Day ◽  
Observation Time ◽  
Series Data ◽  
Clustering Methods ◽  
Content Type ◽  
Free Rider Problem ◽  
Series Analysis

Purpose – Wiki is a new form of information production and organization, which has become one of the most important knowledge resources. In recent years, with the increase of users in wikis, “free rider problem” has been serious. In order to motivate editors to contribute more to a wiki system, it is important to fully understand their contribution behavior. The purpose of this paper is to explore the law of dynamic contribution behavior of editors in wikis. Design/methodology/approach – After developing a dynamic model of contribution behavior, the authors employed both the metrological and clustering methods to process the time series data. The experimental data were collected from Baidu Baike, a renowned Chinese wiki system similar to Wikipedia. Findings – There are four categories of editors: “testers,” “dropouts,” “delayers” and “stickers.” Testers, who contribute the least content and stop contributing rapidly after editing a few articles. After editing a large amount of content, dropouts stop contributing completely. Delayers are the editors who do not stop contributing during the observation time, but they may stop contributing in the near future. Stickers, who keep contributing and edit the most content, are the core editors. In addition, there are significant time-of-day and holiday effects on the number of editors’ contributions. Originality/value – By using the method of time series analysis, some new characteristics of editors and editor types were found. Compared with the former studies, this research also had a larger sample. Therefore, the results are more scientific and representative and can help managers to better optimize the wiki systems and formulate incentive strategies for editors.

scholarly journals Highly comparative time-series analysis: the empirical structure of time series and their methods

2013 ◽  
Vol 10 (83) ◽  
pp. 20130048 ◽  
Author(s):  
Ben D. Fulcher ◽  
Max A. Little ◽  
Nick S. Jones
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Scientific Data ◽  
Series Data ◽  
Series Analysis ◽  
Analysis Methods ◽  
Scientific Disciplines ◽  
History Of ◽  
Reduced Representations

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

Time Series Analysis in Pooled Cross-Sections

1986 ◽  
Vol 2 (3) ◽  
pp. 331-349 ◽  
Author(s):  
John J. Beggs
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Sections ◽  
Spectral Methods ◽  
Time Series Data ◽  
Series Data ◽  
Cross Sectional ◽  
Series Analysis ◽  
Simulation Results ◽  
Spectral Representations

This article proposes the use of spectral methods to pool cross-sectional replications (N) of time series data (T) for time series analysis. Spectral representations readily suggest a weighting scheme to pool the data. The asymptotically desirable properties of the resulting estimators seem to translate satisfactorily into samples as small as T = 25 with N = 5. Simulation results, Monte Carlo results, and an empirical example help confirm this finding. The article concludes that there are many empirical situations where spectral methods canbe used where they were previously eschewed.

Clustering Methodology for Time Series Mining

2009 ◽  
Vol 40 (1) ◽  
pp. 81-86
Author(s):  
Pēteris Grabusts ◽  
Arkady Borisov
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Similarity Measures ◽  
Longest Common Subsequence ◽  
Series Data ◽  
Time Series Clustering ◽  
Series Analysis ◽  
Time Series Mining

Clustering Methodology for Time Series MiningA time series is a sequence of real data, representing the measurements of a real variable at time intervals. Time series analysis is a sufficiently well-known task; however, in recent years research has been carried out with the purpose to try to use clustering for the intentions of time series analysis. The main motivation for representing a time series in the form of clusters is to better represent the main characteristics of the data. The central goal of the present research paper was to investigate clustering methodology for time series data mining, to explore the facilities of time series similarity measures and to use them in the analysis of time series clustering results. More complicated similarity measures include Longest Common Subsequence method (LCSS). In this paper, two tasks have been completed. The first task was to define time series similarity measures. It has been established that LCSS method gives better results in the detection of time series similarity than the Euclidean distance. The second task was to explore the facilities of the classical k-means clustering algorithm in time series clustering. As a result of the experiment a conclusion has been drawn that the results of time series clustering with the help of k-means algorithm correspond to the results obtained with LCSS method, thus the clustering results of the specific time series are adequate.

scholarly journals Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis

2021 ◽  
Vol 12 ◽  
Author(s):  
Sungyul Chang ◽  
Unseok Lee ◽  
Min Jeong Hong ◽  
Yeong Deuk Jo ◽  
Jin-Baek Kim
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Developmental Stages ◽  
Series Data ◽  
Future Application ◽  
Yield Prediction ◽  
Flowering Stage ◽  
Series Analysis

Yield prediction for crops is essential information for food security. A high-throughput phenotyping platform (HTPP) generates the data of the complete life cycle of a plant. However, the data are rarely used for yield prediction because of the lack of quality image analysis methods, yield data associated with HTPP, and the time-series analysis method for yield prediction. To overcome limitations, this study employed multiple deep learning (DL) networks to extract high-quality HTTP data, establish an association between HTTP data and the yield performance of crops, and select essential time intervals using machine learning (ML). The images of Arabidopsis were taken 12 times under environmentally controlled HTPP over 23 days after sowing (DAS). First, the features from images were extracted using DL network U-Net with SE-ResXt101 encoder and divided into early (15–21 DAS) and late (∼21–23 DAS) pre-flowering developmental stages using the physiological characteristics of the Arabidopsis plant. Second, the late pre-flowering stage at 23 DAS can be predicted using the ML algorithm XGBoost, based only on a portion of the early pre-flowering stage (17–21 DAS). This was confirmed using an additional biological experiment (P < 0.01). Finally, the projected area (PA) was estimated into fresh weight (FW), and the correlation coefficient between FW and predicted FW was calculated as 0.85. This was the first study that analyzed time-series data to predict the FW of related but different developmental stages and predict the PA. The results of this study were informative and enabled the understanding of the FW of Arabidopsis or yield of leafy plants and total biomass consumed in vertical farming. Moreover, this study highlighted the reduction of time-series data for examining interesting traits and future application of time-series analysis in various HTPPs.

scholarly journals Forecasting Malaria Cases Using Climatic Factors in Delhi, India: A Time Series Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Varun Kumar ◽  
Abha Mangal ◽  
Sanjeet Panesar ◽  
Geeta Yadav ◽  
Richa Talwar ◽  
...  
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Climatic Factors ◽  
Series Data ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Training Centre ◽  
Series Analysis

Background. Malaria still remains a public health problem in developing countries and changing environmental and climatic factors pose the biggest challenge in fighting against the scourge of malaria. Therefore, the study was designed to forecast malaria cases using climatic factors as predictors in Delhi, India. Methods. The total number of monthly cases of malaria slide positives occurring from January 2006 to December 2013 was taken from the register maintained at the malaria clinic at Rural Health Training Centre (RHTC), Najafgarh, Delhi. Climatic data of monthly mean rainfall, relative humidity, and mean maximum temperature were taken from Regional Meteorological Centre, Delhi. Expert modeler of SPSS ver. 21 was used for analyzing the time series data. Results. Autoregressive integrated moving average, ARIMA (0,1,1) (0,1,0)12, was the best fit model and it could explain 72.5% variability in the time series data. Rainfall (P value = 0.004) and relative humidity (P value = 0.001) were found to be significant predictors for malaria transmission in the study area. Seasonal adjusted factor (SAF) for malaria cases shows peak during the months of August and September. Conclusion. ARIMA models of time series analysis is a simple and reliable tool for producing reliable forecasts for malaria in Delhi, India.

Time Series Analysis of Individual Music Therapy in Dementia

GeroPsych ◽  
2015 ◽  
Vol 28 (3) ◽  
pp. 113-122 ◽  
Author(s):  
Arthur Schall ◽  
Julia Haberstroh ◽  
Johannes Pantel
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Music Therapy ◽  
Time Series Data ◽  
Well Being ◽  
Research Area ◽  
Series Data ◽  
Communication Behavior ◽  
Series Analysis ◽  
Study Results

Abstract. This pilot study evaluated the effects of individual music therapy on communication behavior and emotional well-being in persons with advanced dementia. Contrary to more common qualitative designs in dementia-related music therapy research, we used time series analysis of videographed music therapy sessions. This quantitative statistical method for modeling and explaining time-dependent processual data has not been employed in this research area before. Based on aggregated time series data, the results showed positive and statistically significant intervention effects on participants’ communication behavior, situational well-being, and their expression of positive emotions. The study results indicate that, by choosing appropriate outcomes, video-based time series analysis is sensitive and well suited to displaying the effects of music therapy in dementia care.

Introduction to Time Series Analysis for Organizational Research

2016 ◽  
Vol 20 (1) ◽  
pp. 61-94 ◽  
Author(s):  
Andrew T. Jebb ◽  
Louis Tay
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Moving Average ◽  
Dynamic Change ◽  
Decomposition Methods ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Organizational Research ◽  
Series Analysis

Organizational science has increasingly recognized the need for integrating time into its theories. In parallel, innovations in longitudinal designs and analyses have allowed these theories to be tested. To promote these important advances, the current article introduces time series analysis for organizational research, a set of techniques that has proved essential in many disciplines for understanding dynamic change over time. We begin by describing the various characteristics and components of time series data. Second, we explicate how time series decomposition methods can be used to identify and partition these time series components. Third, we discuss periodogram and spectral analysis for analyzing cycles. Fourth, we discuss the issue of autocorrelation and how different structures of dependency can be identified using graphics and then modeled as autoregressive moving-average (ARMA) processes. Finally, we conclude by describing more time series patterns, the issue of data aggregation, and more sophisticated techniques that were not able to be given proper coverage. Illustrative examples based on topics relevant to organizational research are provided throughout, and a software tutorial in R for these analyses accompanies each section.

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