Comparing indicators of recreational fishing in Port Phillip Bay, Australia, from 2008 to 2011 with variability from a background period (2003–07)

2019 ◽  
Vol 70 (10) ◽  
pp. 1345 ◽  
Author(s):  
Karina L. Ryan ◽  
Simon C. Conron
Keyword(s):  
Time Series Data ◽  
Series Data ◽  
Recreational Fishing ◽  
Commercial Fisheries ◽  
Recreational Fisheries ◽  
Similar Time ◽  
Frequency Distributions ◽  
Background Period ◽  
Catch Rates ◽  
Resource Status

Understanding the effects of anthropogenic disturbances on marine environments that support recreational fisheries requires suitable monitoring data. Although catch rates are frequently considered to inform resource status for commercial fisheries, similar time series data are generally unavailable for recreational fisheries. This study is based on 9 years of data from boat-based recreational fishing surveys in Port Phillip Bay, Australia. Harvest rates and length distributions of the retained catches for three target species were statistically compared over 4 years following commencement of shipping channel dredging (2008–11) with variability from a background period (2003–07). Mean harvest rates were generally within historical ranges, with increases for snapper (Chrysophrys auratus) and King George whiting (Sillaginodes punctatus) and decreases for sand flathead (Platycephalus bassensis). Length–frequency distributions were not significantly different to the background period, except for increased mean length of snapper and King George whiting in 2011. This study demonstrates that data obtained from sampling recreational fishers can provide valuable information to monitor harvested resources and maintain social amenity for recreational fisheries during periods of anthropogenic disturbance. Trends in harvest rates from recreational fishing surveys may also complement catch rates from commercial fisheries for assessments of multisector fisheries or stand-alone assessments for recreational-only fisheries.

A self-adapting multi-LSTM ensemble regression mode for failure prediction of transmission line network from wireless mesh nodes’ data

2020 ◽  
pp. 1-9
Author(s):  
Hongbin Sun ◽  
Mingjun Liu ◽  
Zhejun Qing ◽  
Chandler Miller
Keyword(s):  
Time Series ◽  
Transmission Line ◽  
Time Series Data ◽  
Failure Prediction ◽  
Series Data ◽  
Good Prediction ◽  
Similar Time ◽  
Prediction Ability ◽  
Wireless Mesh ◽  
Ensemble Regression

Transmission lines’ condition monitoring is an important part of smart grid construction. To ensure fast and efficient transmission of data, many mash-based wireless networks devices are adopted to collect status information. Since these nodes are exposed to the natural environment, vulnerable to damage, so it is very necessary to be predicting nodes’ fault. However, these mesh nodes are affected by a variety of complex and time-series factors, and traditional models are difficult to achieve effective failure prediction. To solve this problem, this paper proposes a self-adapting multi-LSTM ensemble regression model for transmission line network’s wireless mesh node failure prediction (MLSTM-FP), through establishes the corresponding relationship between similar time factors and LSTMs, the proposed model can realize multi time series data self-adapting and accurate failure prediction of transmission line network’s wireless mesh nodes, The experimental results show that the proposed method has a good prediction ability than traditional methods.

Short-term wind power forecasts by a synthetical similar time series data mining method

2018 ◽  
Vol 115 ◽  
pp. 575-584 ◽  
Author(s):  
Gaiping Sun ◽  
Chuanwen Jiang ◽  
Pan Cheng ◽  
Yangyang Liu ◽  
Xu Wang ◽  
...  
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Wind Power ◽  
Time Series Data ◽  
Series Data ◽  
Mining Method ◽  
Short Term ◽  
Similar Time ◽  
Time Series Data Mining ◽  
Data Mining Method

Power Consumption Forecasting Scheme for Educational Institutions Based on Analysis of Similar Time Series Data

2017 ◽  
Vol 44 (9) ◽  
pp. 954-965 ◽  
Author(s):  
Jihoon Moon ◽  
Jinwoong Park ◽  
Sanghoon Han ◽  
Eenjun Hwang
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Time Series Data ◽  
Series Data ◽  
Educational Institutions ◽  
Similar Time

scholarly journals Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London

2008 ◽  
Vol 5 (25) ◽  
pp. 885-897 ◽  
Author(s):  
Simon Cauchemez ◽  
Neil M Ferguson
Keyword(s):  
Time Series ◽  
Discrete Time ◽  
Continuous Time ◽  
Generation Time ◽  
Data Augmentation ◽  
Time Series Data ◽  
Observation Interval ◽  
Series Data ◽  
Time Model ◽  
Similar Time

We present a new statistical approach to analyse epidemic time-series data. A major difficulty for inference is that (i) the latent transmission process is partially observed and (ii) observed quantities are further aggregated temporally. We develop a data augmentation strategy to tackle these problems and introduce a diffusion process that mimicks the susceptible–infectious–removed (SIR) epidemic process, but that is more tractable analytically. While methods based on discrete-time models require epidemic and data collection processes to have similar time scales, our approach, based on a continuous-time model, is free of such constraint. Using simulated data, we found that all parameters of the SIR model, including the generation time, were estimated accurately if the observation interval was less than 2.5 times the generation time of the disease. Previous discrete-time TSIR models have been unable to estimate generation times, given that they assume the generation time is equal to the observation interval. However, we were unable to estimate the generation time of measles accurately from historical data. This indicates that simple models assuming homogenous mixing (even with age structure) of the type which are standard in mathematical epidemiology miss key features of epidemics in large populations.

Recreational Fisheries

Overfishing ◽  
2012 ◽  
Author(s):  
Ray Hilborn ◽  
Ulrike Hilborn
Keyword(s):  
Recreational Fishing ◽  
Commercial Fisheries ◽  
Recreational Fisheries

Are recreational fisheries fundamentally different from commercial fisheries? The simple answer is yes, recreational fishing is very different from most commercial fisheries. Many more people participate, their catch and effort are harder to measure, and the overall objective of recreational fishing is often very...

scholarly journals Comparative Angler Catches of Australian Bass (Macquaria novemaculeata) in Three Major River Systems in New South Wales, Australia

2005 ◽  
Vol 18 (2) ◽  
Author(s):  
B. VAN DER WALT ◽  
R. A. FARAGHER ◽  
J. HARRIS
Keyword(s):  
Time Series Data ◽  
New South ◽  
New South Wales ◽  
River System ◽  
Size Composition ◽  
Series Data ◽  
Severe Drought ◽  
Catch Per Unit Effort ◽  
South Wales ◽  
Catch Rates

A joint program between New South Wales Fisheries and three fishing clubs was initiated in 1988 with the aim of collecting standardised catch and effort data during biannual fishing competitions on three major rivers in New South Wales. This paper examines the data to determine trends in the catch of the target species, Australian bass (Macquaria novemaculeata) and to evaluate whether the data can be used to assess Australian bass populations over time. Distinct trends in Australian bass mean length in each river system were evident but catch rates were more variable. Median catch per unit effort was similar (mostly between 0.5 and 1.5 Australian bass·h-1 ) in the Nepean and Williams Rivers although catch rates in the Manning River were nearly always zero. There was an increasing trend in the mean length of Australian bass in all three rivers, possibly representing a recovery in fish populations following severe drought from 1979 to 1983. Low or zero catch rates were continually recorded in the Manning River and size composition data indicated a lack of recruitment through most of the study period. The standardised format of the data collection program provided qualitative and reliable time series data allowing the determination of long-term trends in the population structure of Australian bass which can be used for monitoring and management purposes.

Time Series Data Mining

2014 ◽  
Vol 1 (4) ◽  
pp. 51-68 ◽  
Author(s):  
Daniel Hebert ◽  
Billie Anderson ◽  
Alan Olinsky ◽  
J. Michael Hardin
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Grocery Store ◽  
Series Data ◽  
Time Interval ◽  
Real World Data ◽  
Similar Time ◽  
Data Set ◽  
Time Series Data Mining

Modern technologies have allowed for the amassment of data at a rate never encountered before. Organizations are now able to routinely collect and process massive volumes of data. A plethora of regularly collected information can be ordered using an appropriate time interval. The data would thus be developed into a time series. Time series data mining methodology identifies commonalities between sets of time-ordered data. Time series data mining detects similar time series using a technique known as dynamic time warping (DTW). This research provides a practical application of time series data mining. A real-world data set was provided to the authors by dunnhumby. A time series data mining analysis is performed using retail grocery store chain data and results are provided.

scholarly journals Status-quo management of marine recreational fisheries undermines angler welfare

2018 ◽  
Vol 115 (36) ◽  
pp. 8948-8953 ◽  
Author(s):  
Joshua K. Abbott ◽  
Patrick Lloyd-Smith ◽  
Daniel Willard ◽  
Wiktor Adamowicz
Keyword(s):  
Economic Value ◽  
Status Quo ◽  
Seasonal Distribution ◽  
Red Snapper ◽  
Recreational Fishing ◽  
Commercial Fisheries ◽  
Recreational Fisheries ◽  
Global Context ◽  
The Us ◽  
Management Approaches

Recreational fisheries can have a significant impact on fish populations and can suffer from the same symptoms of open access as commercial fisheries. However, recreational fisheries receive little attention compared with their commercial counterparts. Regulations designed to allocate scarce fish, such as seasonal closures and bag limits, can result in significant losses of value to anglers. We provide an estimate of these foregone benefits by estimating the potential gains to implementing management reforms of the headboat portion of the recreational red snapper fishery in the US Gulf of Mexico. This fishery has suffered from a regulatory spiral of shortened seasons and lowered bag limits in spite of rebuilding stocks. We gather primary survey data of headboat anglers that elicit trip behavior and their planned number and seasonal distribution of trips under status-quo and alternative management approaches. We use these data to estimate a model of anglers’ seasonal trip demand as a function of the ability to retain red snapper, bag limits, and fees. We find that a hypothetical rights-based policy, whereby vessels with secure rights to a portion of annual catch could offer their customers year-round fishing in exchange for lower per-angler retention and increased fees, could raise the average angler’s welfare by $139/y. When placed in the global context of recreational fishing, these estimates suggest that status-quo management may deprive anglers of billions of dollars of lost economic value per year.

Feature-aware forecasting of large-scale time series data sets

2020 ◽  
Vol 62 (3-4) ◽  
pp. 157-168
Author(s):  
Claudio Hartmann ◽  
Lars Kegel ◽  
Wolfgang Lehner
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Missing Values ◽  
Time Series Data ◽  
Series Data ◽  
The Internet ◽  
Data Sets ◽  
Similar Time ◽  
Forecast Time ◽  
The Internet Of Things

AbstractThe Internet of Things (IoT) sparks a revolution in time series forecasting. Traditional techniques forecast time series individually, which becomes unfeasible when the focus changes to thousands of time series exhibiting anomalies like noise and missing values. This work presents CSAR, a technique forecasting a set of time series with only one model, and a feature-aware partitioning applying CSAR on subsets of similar time series. These techniques provide accurate forecasts a hundred times faster than traditional techniques, preparing forecasting for the arising challenges of the IoT era.

Recreational Fishing

2019 ◽  
pp. 83-91
Author(s):  
Ray Hilborn ◽  
Ulrike Hilborn
Keyword(s):  
Economic Value ◽  
Developed Countries ◽  
Fishing Effort ◽  
Recreational Fishing ◽  
Commercial Fisheries ◽  
Recreational Fisheries ◽  
The World ◽  
The Developed Countries ◽  
Do So

Recreational Fishing. Recreational fisheries involve far more people than commercial fisheries do in most of the developed countries, and their economic value is often estimated to be larger than that of commercial fisheries. The main objective of most recreational fisheries is the opportunity to fish rather than the number or value of fish caught, and very different management methods are appropriate. Ideally, the total fishing effort should be maximized rather than reduced, to keep costs down as for commercial fisheries. Access to recreational fishing differs greatly around the world and ranges from effectively privatizing fishing opportunity to allowing anyone wishing to fish to do so.

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