Evaluating Forecasting Procedures for Predicting Pacific Herring (Clupea harengus pallasi) Recruitment in British Columbia

1988 ◽  
Vol 45 (6) ◽  
pp. 928-935 ◽  
Author(s):  
M. Stocker ◽  
D. J. Noakes
Keyword(s):  
Time Series ◽  
Clupea Harengus ◽  
Environmental Data ◽  
Forecast Errors ◽  
Pacific Herring ◽  
Time Step ◽  
Absolute Deviation ◽  
Percent Error ◽  
One Step ◽  
Absolute Percent Error

The ability of four forecasting methods to generate one-step-ahead forecasts of Pacific herring (Clupea harengus pillasi) recruitment is considered in this paper. Recruitment time series for five coastal stocks and various environmental time series are employed in the analyses. Information up to and including time t is employed to estimate the parameters of each model used to forecast recruitment in year t + 1. Parameter estimates are then updated after each time step with a total of seven one-step-ahead forecasts being generated by each model for each stock. The forecast errors are compared using the five criteria: (1) root mean squared error, (2) mean absolute deviation, (3) mean absolute percent error, (4) median absolute deviation, and (5) median absolute percent error. The results of the study indicate that time series models may provide better forecasts of recruitment for the Strait of Georgia/Johnstone Strait stocks than the other competing procedures. A Ricker stock–recruitment model that takes into account environmental data appears to produce marginally better forecasts for the Central Coast and Queen Charlotte Island stocks, while all models produced equally good/bad forecasts for the Prince Rupert district stocks.

scholarly journals Peramalan Penerbitan Ijin Mendirikan Bangunan Dengan Single Moving Average Dan Exponential Smoothing

2019 ◽  
Vol 18 (2) ◽  
Author(s):  
Yogha Pramana ◽  
Rukmi Sari Hartati ◽  
Komang Oka Saputra
Keyword(s):  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Percent Error ◽  
Mean Absolute Percent Error ◽  
Mean Error ◽  
Absolute Percent Error

Ijin Mendirikan Bangunan adalah ijin yang diberikan oleh Kepala Daerah pada pemilik bangunan untuk mendirikan bangunan, mengubah, memperluas, mengurangi atau merawat bangunan sesuai dengan persyaratan administratif dan persyaratan teknis yang berlaku. Peramalan adalah merupakan perkiraan mengenai terjadinya suatu kejadian pada masa depan. Peramalan merupakan sebuah alat bantu yang penting dalam perencanaan yang efesien dan efektif. Prosesnya untuk mengetahui kebutuhan di masa datang antara lain kebutuhan ukuran kuantitas, kualitas, waktu dan lokasi untuk pemenuhan permintaan barang ataupun jasa. Peramalan merupakan bagian awal dari pengambilan suatu keputusan akhir. Data Ijin Mendirikan Bangunan (IMB) di hitung dengan metode Simple Moving Average dan Exponential Smoothing untuk mengetahui nilai dari Mean Error, Mean Absolute Deviation, Mean Square Error, Standar Error, Mean Absolute Percent Error.

scholarly journals Issues on Applying One- and Multi-Step Numerical Methods to Chaotic Oscillators for FPGA Implementation

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 151
Author(s):  
Omar Guillén-Fernández ◽  
María Fernanda Moreno-López ◽  
Esteban Tlelo-Cuautle
Keyword(s):  
Time Series ◽  
Numerical Methods ◽  
Numerical Method ◽  
Euler Method ◽  
Fpga Implementation ◽  
Chaotic Time Series ◽  
Time Step ◽  
Chaotic Oscillators ◽  
One Step ◽  
Selection Of

Chaotic oscillators have been designed with embedded systems like field-programmable gate arrays (FPGAs), and applied in different engineering areas. However, the majority of works do not detail the issues when choosing a numerical method and the associated electronic implementation. In this manner, we show the FPGA implementation of chaotic and hyper-chaotic oscillators from the selection of a one-step or multi-step numerical method. We highlight that one challenge is the selection of the time-step h to increase the frequency of operation. The case studies include the application of three one-step and three multi-step numerical methods to simulate three chaotic and two hyper-chaotic oscillators. The numerical methods provide similar chaotic time-series, which are used within a time-series analyzer (TISEAN) to evaluate the Lyapunov exponents and Kaplan–Yorke dimension (DKY) of the (hyper-)chaotic oscillators. The oscillators providing higher exponents and DKY are chosen because higher values mean that the chaotic time series may be more random to find applications in chaotic secure communications. In addition, we choose representative numerical methods to perform their FPGA implementation, which hardware resources are described and counted. It is highlighted that the Forward Euler method requires the lowest hardware resources, but it has lower stability and exactness compared to other one-step and multi-step methods.

scholarly journals Fuzzy Time Series for Projecting School Enrolment in Malaysia

2021 ◽  
Vol 6 (1) ◽  
pp. 11-21
Author(s):  
Nor Hayati Shafii ◽  
Rohana Alias ◽  
Siti Rohani Shamsuddin ◽  
Diana Sirmayunie Mohd Nasir
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Absolute Deviation ◽  
Error Measure ◽  
Squared Error ◽  
Complete Procedure ◽  
The Mean ◽  
Absolute Percent Error

There are a variety of approaches to the problem of predicting educational enrolment.  However, none of them can be used when the historical data are linguistic values.  Fuzzy time series is an efficient and effective tool to deal with such problems. In this paper, the forecast of the enrolment of pre-primary, primary, secondary, and tertiary schools in Malaysia is carried out using fuzzy time series approaches. A fuzzy time series model is developed using historical dataset collected from the United Nations Educational, Scientific, and Cultural Organization (UNESCO) from the year 1981 to 2018.  A complete procedure is proposed which includes: fuzzifying the historical dataset, developing a fuzzy time series model, and calculating and interpreting the outputs. The accuracy of the model are also examined to evaluate how good the developed forecasting model is. It is tested based on the value of the mean squared error (MSE), Mean Absolute Percent Error (MAPE) and Mean Absolute Deviation (MAD).  The lower the value of error measure, the higher the accuracy of the model.  The result shows that fuzzy time series model developed for primary school enrollments is the most accurate with the lowest error measure, with the MSE value being 0.38, MAPE 0.43 and MAD 0.43 respectively.

scholarly journals Prediksi Tingkat Produksi Buah Kelapa Sawit dengan Metode Single Moving Average

2021 ◽  
pp. 251-256
Author(s):  
Feri Irawan ◽  
S Sumijan ◽  
Y Yuhandri
Keyword(s):  
Oil Palm ◽  
Mean Squared Error ◽  
Moving Average ◽  
Fruit Production ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Palm Fruit ◽  
Percent Error ◽  
Mean Absolute Percent Error ◽  
Absolute Percent Error

Palm oil is one of the largest agricultural products in Indonesia and has a high economic value and can improve the welfare of oil palm farmers. The amount of oil palm fruit production is not always stable or increasing, but increases up and down which is influenced by many factors. This study aims to estimate the average amount of oil palm fruit production every year and prepare anticipatory steps in the event of a decrease in oil palm fruit production. The image processed in this study was the production of palm fruit in a few years which was generated from the results of oil palm plantations. Furthermore, data is processed using the Single Moving Avarage method. This method is a method of forecasting or predictions using a number of actual data to generate predictive values ​​in the future. The results of testing on the single moving average method can be seen forecasts of oil palm fruit production in 2021 using Moving Averge 3 of 200.749 tons with Mean Absolute Deviation 19.604, Mean Squared Error  456.963.281  and Mean Absolute Percent Error 10,0%. Moving Averge 4 was  206.771 tons with the Mean Absolute Deviation  27.333, Mean Squared Error  752.202.579 and Mean Absolute Percent Error 14,2%. Moving Averge 5 was  210.908 tons with Mean Absolute Deviation  26.890, Mean Squared Error  723.072.100 and Mean Absolute Percent Error 14.1%. The test results using the Single Moving Average method can be concluded that forecasting using Moving Average 3 can be used because the relative error level is smaller than Moving Average 4 and 5, with the value of the Mean Absolute Percent error of 10.0% and Mean Absolute Deviation 19.604.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

scholarly journals Adult body growth and reproductive investment vary markedly within and across Atlantic and Pacific herring: a meta-analysis and review of 26 stocks

2021 ◽  
Author(s):  
Thassya C. dos Santos Schmidt ◽  
Doug E. Hay ◽  
Svein Sundby ◽  
Jennifer A. Devine ◽  
Guðmundur J. Óskarsson ◽  
...  
Keyword(s):  
Egg Size ◽  
Meta Analysis ◽  
Reproductive Investment ◽  
Body Growth ◽  
Clupea Harengus ◽  
Pacific Herring ◽  
Atlantic Herring ◽  
Additional Information ◽  
Trade Offs ◽  
Investment In Reproduction

AbstractLife-history traits of Pacific (Clupea pallasii) and Atlantic (Clupea harengus) herring, comprising both local and oceanic stocks subdivided into summer-autumn and spring spawners, were extensively reviewed. The main parameters investigated were body growth, condition, and reproductive investment. Body size of Pacific herring increased with increasing latitude. This pattern was inconsistent for Atlantic herring. Pacific and local Norwegian herring showed comparable body conditions, whereas oceanic Atlantic herring generally appeared stouter. Among Atlantic herring, summer and autumn spawners produced many small eggs compared to spring spawners, which had fewer but larger eggs—findings agreeing with statements given several decades ago. The 26 herring stocks we analysed, when combined across distant waters, showed clear evidence of a trade-off between fecundity and egg size. The size-specific individual variation, often ignored, was substantial. Additional information on biometrics clarified that oceanic stocks were generally larger and had longer life spans than local herring stocks, probably related to their longer feeding migrations. Body condition was only weakly, positively related to assumingly in situ annual temperatures (0–30 m depth). Contrarily, body growth (cm × y−1), taken as an integrator of ambient environmental conditions, closely reflected the extent of investment in reproduction. Overall, Pacific and local Norwegian herring tended to cluster based on morphometric and reproductive features, whereas oceanic Atlantic herring clustered separately. Our work underlines that herring stocks are uniquely adapted to their habitats in terms of trade-offs between fecundity and egg size whereas reproductive investment mimics the productivity of the water in question.

scholarly journals Evolution of Burned Area in Forest Fires under Climate Change Conditions in Southern Spain Using ANN

2019 ◽  
Vol 9 (19) ◽  
pp. 4155
Author(s):  
Pérez-Sánchez ◽  
Jimeno-Sáez ◽  
Senent-Aparicio ◽  
Díaz-Palmero ◽  
de Dios Cabezas-Cerezo
Keyword(s):  
Climate Change ◽  
Forest Fires ◽  
Southern Spain ◽  
Burned Area ◽  
Change Scenario ◽  
Absolute Deviation ◽  
Average Temperature ◽  
Mediterranean Regions ◽  
Artificial Neural Network Ann ◽  
Absolute Percent Error

Wildfires in Mediterranean regions have become a serious problem, and it is currently the main cause of forest loss. Numerous prediction methods have been applied worldwide to estimate future fire activity and area burned in order to provide a stable basis for future allocation of fire-fighting resources. The present study investigated the performance of an artificial neural network (ANN) in burned area size prediction and to assess the evolution of future wildfires and the area concerned under climate change in southern Spain. The study area comprised 39.41 km2 of land burned from 2000 to 2014. ANNs were used in two subsequential phases: classifying the size of the wildfires and predicting the burned surface for fires larger than 30,000 m2. Matrix of confusion and 10-fold cross-validations were used to evaluate ANN classification and mean absolute deviation, root mean square error, mean absolute percent error and bias, which were the metrics used for burned area prediction. The success rate achieved was above 60–70% depending on the zone. An average temperature increase of 3 °C and a 20% increase in wind speed during 2071–2100 results in a significant increase of the number of fires, up to triple the current figure, resulting in seven times the average yearly burned surface depending on the zone and the climate change scenario.

scholarly journals Assessing dry weather flow contribution in TSS and COD storm events loads in combined sewer systems

2011 ◽  
Vol 63 (12) ◽  
pp. 2983-2991 ◽  
Author(s):  
M. Métadier ◽  
J. L. Bertrand-Krajewski
Keyword(s):  
Time Series ◽  
Oxygen Demand ◽  
Seasonal Effect ◽  
Storm Events ◽  
Combined System ◽  
Time Step ◽  
Urban Catchment ◽  
Sewer Systems ◽  
Continuous Discharge

Continuous high resolution long term turbidity measurements along with continuous discharge measurements are now recognised as an appropriate technique for the estimation of in sewer total suspended solids (TSS) and Chemical Oxygen Demand (COD) loads during storm events. In the combined system of the Ecully urban catchment (Lyon, France), this technique is implemented since 2003, with more than 200 storm events monitored. This paper presents a method for the estimation of the dry weather (DW) contribution to measured total TSS and COD event loads with special attention devoted to uncertainties assessment. The method accounts for the dynamics of both discharge and turbidity time series at two minutes time step. The study is based on 180 DW days monitored in 2007–2008. Three distinct classes of DW days were evidenced. Variability analysis and quantification showed that no seasonal effect and no trend over the year were detectable. The law of propagation of uncertainties is applicable for uncertainties estimation. The method has then been applied to all measured storm events. This study confirms the interest of long term continuous discharge and turbidity time series in sewer systems, especially in the perspective of wet weather quality modelling.

Fixation Shrinkage of Herring Larvae: Effects of Salinity, Formalin Concentration, and Other Factors

1982 ◽  
Vol 39 (8) ◽  
pp. 1138-1143 ◽  
Author(s):  
D. E. Hay
Keyword(s):  
Clupea Harengus ◽  
Pacific Herring ◽  
Larval Size ◽  
Factors Affecting ◽  
Clupea Harengus Pallasi

The most important factors affecting the degree of larval shrinkage of Pacific herring (Clupea harengus pallasi) larvae during fixation are the salinity and formalin concentrations and initial larval size. In low formalin concentrations (2–5% formalin) shrinkage increased from less than 2% shrinkage at low salinities to about 10% shrinkage in seawater formalin. In high formalin concentrations (20–30% formalin) shrinkage was fairly uniform, ranging from about 3% shrinkage in low salinities to about 5% in seawater. Shrinkage in fixatives stored at 0, 5, 10, 20, and 30 °C was slightly higher (1–2%) at the higher temperatures. Buffering agents and starvation had no effect on shrinkage. Small, young larvae shrank relatively more than larger older larvae.Key words: herring larvae, fixation, shrinkage, formalin, salinity

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