scholarly journals Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 39-55
Author(s):  
Rodgers Makwinja ◽  
Seyoum Mengistou ◽  
Emmanuel Kaunda ◽  
Tena Alemiew ◽  
Titus Bandulo Phiri ◽  
...  
Keyword(s):  
Time Series ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Annual Fish ◽  
Unit Effort ◽  
The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

Predicting catch per unit effort from a multispecies commercial fishery in Port Phillip Bay, Australia

2020 ◽  
Vol 71 (4) ◽  
pp. 542
Author(s):  
Karina L. Ryan ◽  
Denny Meyer
Keyword(s):  
Time Series ◽  
Environmental Conditions ◽  
Environmental Variability ◽  
Moving Average ◽  
Commercial Fishing ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Explanatory Variables ◽  
Unit Effort ◽  
Stock Abundance

Quantitative models that predict stock abundance can inform stock assessments and adaptive management that allows for less stringent controls when abundance is high and environmental conditions are suitable, or tightening controls when abundance is low and environmental conditions are least suitable. Absolute estimates of stock abundance are difficult and expensive to obtain, but data from routine reporting in commercial fisheries logbooks can provide an indicator of stock status. Autoregressive integrated moving average (ARIMA) models were constructed using catch per unit effort (CPUE) from commercial fishing in Port Phillip Bay from 1978–79 to 2009–10. Univariate and multivariate models were compared for short-lived species (Sepioteuthis australis), and species represented by 1–2 year-classes (Sillaginodes punctatus) and 5–6 year-classes (Chrysophrys auratus). Simple transfer models incorporating environmental variables produced the best predictive models for all species. Multivariate ARIMA models are dependent on the availability of an appropriate time series of explanatory variables. This study demonstrates an application of time series methods to predict monthly CPUE that is relevant to fisheries for species that are short lived or vulnerable to fishing during short phases in their life history or where high intra-annual variation in stock abundance occurs through environmental variability.

scholarly journals Equity Return Modeling and Prediction Using Hybrid ARIMA-GARCH Model

2017 ◽  
Vol 8 (3) ◽  
pp. 154
Author(s):  
Kaiying Sun
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Model Fitting ◽  
Garch Model ◽  
Information Criterion ◽  
Equity Returns ◽  
Series Data ◽  
Arima Models ◽  
Fitting Procedure

In this paper, a hybrid ARIMA-GARCH model is proposed to model and predict the equity returns for three US benchmark indices: Dow Transportation, S&P 500 and VIX. Equity returns are univariate time series data sets, one of the methods to predict them is using the Auto-Regressive Integrated Moving Average (ARIMA) models. Despite the fact that the ARIMA models are powerful and flexible, they are not be able to handle the volatility and nonlinearity that are present in the time series data. However, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models are designed to capture volatility clustering behavior in time series. In this paper, we provide motivations and descriptions of the hybrid ARIMA-GARCH model. A complete data analysis procedure that involves a series of hypothesis testings and a model fitting procedure using the Akaike Information Criterion (AIC) is provided in this paper as well. Simulation results of out of sample predictions are also provided in this paper as a reference.

scholarly journals Distance variable improvement of time-series big data stream evaluation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Standard Method ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Traffic Demand ◽  
Incremental Model ◽  
Chernoff Bound ◽  
The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

Wind-Aided Recruitment of Canadian Arctic Cisco (Coregonus autumnalis) into Alaskan Waters

1988 ◽  
Vol 45 (5) ◽  
pp. 906-910 ◽  
Author(s):  
Robert G. Fechhelm ◽  
David B. Fissel
Keyword(s):  
Time Series ◽  
Canadian Arctic ◽  
Wind Data ◽  
Commercial Fishery ◽  
Catch Per Unit Effort ◽  
Longshore Currents ◽  
Unit Effort ◽  
Colville River ◽  
Young Of The Year ◽  
Mackenzie River

Summer wind data collected at Barter Island, Alaska, were compared with commercial fishery catches of arctic cisco (Coregonus autumnalis) at the Colville River, Alaska, for the period 1967–85. There was a significant (p = 0.036) association between yearly catch-per-unit-effort and the percent of easterly winds after adjusting for a 5-yr differential in the two time series. Results suggest that young-of-the-year fish which spawn in Canada's Mackenzie River are aided in their westward dispersal into Alaskan waters via wind-driven longshore currents. The greater the prevalence of easterly winds (westerly currents), the greater the recruitment. Increased recruitment manifests itself as an increase in Alaskan commercial fishery catch some 5-yr later when fish have grown to a size that renders them susceptible to commercial nets.

scholarly journals Time series data analysis and ARIMA modeling to forecast the short-term trajectory of the acceleration of fatalities in Brazil caused by the corona virus (COVID-19)

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11748
Author(s):  
Akini James ◽  
Vrijesh Tripathi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Explanatory Variable ◽  
Arima Model ◽  
Series Data ◽  
Arima Models ◽  
Composite Function ◽  
Short Run ◽  
Lag Model ◽  
Time Series Data Analysis

Objective This paper incorporates the concept of acceleration to fatalities caused by the coronavirus in Brazil from time series data beginning on 17th March 2020 (the day of the first death) to 3rd February 2021 to explain the trajectory of the fatalities for the next six months using confirmed infections as the explanatory variable. Methods Acceleration of the cases of confirmed infection and fatalities were calculated by using the concept of derivatives. Acceleration of fatality function was then determined from multivariate linear function and calculus chain rule for composite function with confirmed infections as an explanatory variable. Different ARIMA models were fitted for each acceleration of fatality function: the de-seasonalized Auto ARIMA Model, the adjusted lag model, and the auto ARIMA model with seasonality. The ARIMA models were validated. The most realistic models were selected for each function for forecasting. Finally, the short run six-month forecast was conducted on the trajectory of the acceleration of fatalities for all the selected best ARIMA models. Results It was found that the best ARIMA model for the acceleration functions were the seasonalized models. All functions suggest a general decrease in fatalities and the pace at which this change occurs will eventually slow down over the next six months. Conclusion The decreasing fatalities over the next six-month period takes into consideration the direct impact of the confirmed infections. There is an early increase in acceleration for the forecast period, which suggests an increase in daily fatalities. The acceleration eventually reduces over the six-month period which shows that fatalities will eventually decrease. This gives health officials an idea on how the fatalities will be affected in the future as the trajectory of confirmed COVID-19 infections change.

DOI: https://doi.org/10.36873/ae , Frits Fahridws Damanik

2020 ◽  
Vol 13 (02) ◽  
pp. 1-8
Author(s):  
Agrienvi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Arima Model ◽  
Series Data ◽  
Production Data ◽  
Policy Makers ◽  
Strategic Value ◽  
Modeling And Forecasting ◽  
The Mean ◽  
Arima Modeling

ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean so thatmust differenced while the data on the production of small chilli carried out the stages of data transformation anddifferencing due to the unstationer of data on variants and the mean. The best ARIMA model that can be applied basedon the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in Central KalimantanProvince is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.

F. ANALISIS PENGARUH PAD DAN DAK TERHADAP BELANJA MODAL PROVINSI JAWA TIMUR TAHUN 2010-2019

2021 ◽  
Vol 4 (1) ◽  
pp. 61
Author(s):  
Putri Indah Sari ◽  
Dr. Ignatia Martha Hendrati, S.E., M.E. ◽  
Kiki Asmara,S.E.,MM
Keyword(s):  
Time Series ◽  
Local Governments ◽  
Time Series Data ◽  
Capital Expenditure ◽  
Linear Regression Analysis ◽  
Secondary Data ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Capital Expenditures ◽  
Positive Effect

Abstrak Undang-Undang Nomor 32 Tahun 2004 tentang Otonomi daerah atau Desentralisasi menjelaskan bahwa kewajiban pemerintah daerah dalam mengendalikan daerahnya sesuai dengan aturan dan undang-undang yang berlaku. Pengalokasian Anggaran Belanja Modal didasarkan pada kebutuhan sarana dan prasarana daerah, anggaran Belanja Modal sebaiknya dialokasikan untuk hal-hal yang produktif. Sehingga, pemerintah daerah harus mampu mengalokasikan anggaran belanja modal dengan benar karena hal itu merupakan salah satu langkah pemerintah daerah dalam meningkatkan pelayanan publik. Penelitian ini bertujuan untuk menguji pengaruh dari Pendapatan Asli Daerah (PAD)  dan Dana Alokasi Khusus (DAK) terhadap Belanja Modal Provinsi Jawa Timur. Penelitian ini menggunakan analisis data time series Tahun 2015-2019 di Provinsi Jawa Timur. Data yang digunakan merupakan data sekunder yang diperoleh dari Direktorat Jenderal Perimbangan Keuangan Republik Indonesia. Metode analisis yang digunakan adalah Analisis Regresi linier berganda, Uji koefisien Determinasi (R2), Uji-t dan Uji F dengan bantuan software SPSS. Dari hasil penelitian menunjukkan bahwa Pendapatan Asli Daerah dan Dana Alokasi Khusus secara (simultan) mempunyai pengaruh signifikan terhadap Belanja Modal di Provinsi Jawa Timur Tahun 2010-2019. Secara parsial 1) Pendapatan Asli Daerah berpengaruh positif terhadap Belanja Modal Provinsi Jawa Timur Tahun 2010-2019. 2) Dana Alokasi Khusus berpengaruh positif  variabel PAD berpengaruh positif terhadap Belanja Modal Provinsi Jawa Timur Tahun 2010-2019.   Kata kunci : Belanja Modal, PAD, dan DAK. Abstract Law Number 32 of 2004 concerning Regional Autonomy or Decentralization explains that the obligation of local governments to control their regions is in accordance with the applicable laws and regulations. The allocation of the Capital Expenditure Budget is based on the needs of regional facilities and infrastructure, the capital expenditure budget should be allocated for productive things. Thus, local governments must be able to allocate the capital expenditure budget properly because this is one of the steps of the local government in improving public services. This study aims to examine the effect of Regional Original Income (PAD) and Special Allocation Funds (DAK) on the Capital Expenditure of East Java Province. This study uses time series data analysis 2015-2019 in East Java Province. The data used is secondary data obtained from the Directorate General of Fiscal Balance of the Republic of Indonesia. The analytical method used is multiple linear regression analysis, coefficient of determination (R2), t-test and F test with the help of SPSS software. The results of the study indicate that the Regional Original Income and the Special Allocation Funds (simultaneously) have a significant effect on capital expenditure in East Java Province in 2010-2019. Partially 1) Local Own Revenue has a positive effect on the Capital Expenditures of East Java Province in 2010-2019. 2) The Special Allocation Fund has a positive effect, the PAD variable has a positive effect on the Capital Expenditure of East Java Province in 2010-2019. Keywords: Capital Expenditures, PAD, and DAK

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