FORECASTING FRESH WATER AND MARINE FISH PRODUCTION IN MALAYSIA USING ARIMA AND ARFIMA MODELS

2018 ◽  
Vol 3 (2) ◽  
pp. 81
Author(s):  
P J W Mah ◽  
N A M Ihwal ◽  
N Z Azizan
Keyword(s):  
Time Series ◽  
Marine Fish ◽  
Model Fitting ◽  
Forecast Model ◽  
Absolute Error ◽  
Percentage Error ◽  
Human Consumption ◽  
Fish Production ◽  
Arfima Models ◽  
Time Series Modelling

Malaysia is surrounded by sea, rivers and lakes which provide natural sources of fish for human consumption. Hence, fish is one source of protein supply to the country and fishery is a sub-sector that contribute to the national gross domestic product. Since fish forecasting is crucial in fisheries management for managers and scientists, time series modelling can be one useful tool. Time series modelling have been used in many fields of studies including the fields of fisheries. In a previous research, the ARIMA and ARFIMA models were used to model marine fish production in Malaysia and the ARFIMA model emerged to be a better forecast model. In this study, we consider fitting the ARIMA and ARFIMA to both the marine and freshwater fish production in Malaysia. The process of model fitting was done using the “ITSM 2000, version 7.0” software. The performance of the models were evaluated using the mean absolute error, root mean square error and mean absolute percentage error. It was found in this study that the selection of the best fit model depends on the forecast accuracy measures used.

scholarly journals Information technology for forecasting the financial results of insurance companies

2021 ◽  
Vol 3 (2) ◽  
pp. 87-93
Author(s):  
K. M. Berezka ◽  
◽  
O. V. Kneysler ◽  
N. Ya. Spasiv ◽  
H. M. Kulyna ◽  
...  
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Mean Value ◽  
Insurance Companies ◽  
Rational Approach ◽  
Percentage Error ◽  
Software Module ◽  
Financial Decisions ◽  
Time Series Modelling ◽  
Financial Result

The purpose of time series modelling is to predict future indicators based on the study and analysis of past and present data. Various time series methods are used for forecasting. The article uses econometric extrapolation research methods. Analyzed scientific works are related to extrapolation methods for forecasting time series. The dynamics of the financial formation related to results of Ukrainian insurance companies by the types of their activities have been analyzed. The main factors that determine the effectiveness are determined. It was found that the most rational approach to short-term forecasting of the financial results of insurers is the use of exponential smoothing. The optimal parameters are selected for the model of exponential smoothing of the first and second order by the method on the grid. The following indicators of the quality of the model were used: the mean value of the standard deviation of the model error to the actual data, Theils coefficient of discrepancy, mean absolute percentage error MARE. The net financial result of the activities of Ukrainian insurers was predicted, the lower and upper bounds of the forecast for 2021 for a reliability level of 0.95. To predict the net financial result of the activities of Ukrainian insurers, statistical data for 10 years from 2011 to 2020 were used, the financial results of the main (insurance and other operating) activities before tax, the results of financial activities before tax, the financial results of other ordinary activities (extraordinary events) before tax, income tax. The prototype of the software module for predicting the financial performance of insurance companies was developed in Statistica and Excel. Forecasting results based on the use of econometric modelling make it possible to identify permanent positive shifts in the domestic insurance market and the activities of insurers on it; to confirm the effectiveness of the adopted strategic and tactical financial decisions of insurance companies; to increase the efficiency of insurers management based on the results of quantitative determination the degree of influence of each factor on the formation of the financial results related to their activities; to identify trends in the development of the situation in the future, to more accurately form a set of measures to maximize profits and minimize costs of insurance companies to ensure guarantees of reliable insurance protection and satisfy the interests of their owners. Keywords: financial results; insurance companies; net financial result; exponential smoothing; time series; econometric forecasting methods.

Short-term Forecasting in Marine Fish Stocks

1981 ◽  
Vol 38 (10) ◽  
pp. 1247-1254 ◽  
Author(s):  
Max Stocker ◽  
Ray Hilborn
Keyword(s):  
Time Series ◽  
Marine Fish ◽  
Management Practice ◽  
Model Fitting ◽  
Production Model ◽  
Catch Per Unit Effort ◽  
Short Term ◽  
Fish Stocks ◽  
Production Models ◽  
The Difference

The predictive power of stock production models and simple time series methods was considered for five marine fish stocks. The distinction between model fitting and forecasting future short-term catch is discussed, as is the difference between techniques to forecast short-term yield, and techniques to calculate long-term management practice. Fox's procedure for fitting Pella and Tomlinson's stock production model, Schnute's method for fitting Schaefer's model, and Gulland's method are all considered. We found that all methods except that of Gulland work well for some stocks, and the relative performance of the methods depends upon the exploitation history of the stock. In several instances one of the best forecasts of next year's catch per unit effort (CPUE) was the previous year's CPUE, emphasizing the fact that a good forecasting technique may have no utility in determining management policies.Key words: production models, catch and effort, fisheries, management, catch forecasting, time series

scholarly journals Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1942
Author(s):  
Pyae Pyae Phyo ◽  
Yung-Cheol Byun
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Absolute Error ◽  
Percentage Error ◽  
Ensemble Model ◽  
Load Prediction ◽  
Energy Prediction ◽  
The Times ◽  
One Year

The energy manufacturers are required to produce an accurate amount of energy by meeting the energy requirements at the end-user side. Consequently, energy prediction becomes an essential role in the electric industrial zone. In this paper, we propose the hybrid ensemble deep learning model, which combines multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM to improve the forecasting performance. These DL architectures are more popular and better than other machine learning (ML) models for time series electrical load prediction. Therefore, hourly-based energy data are collected from Jeju Island, South Korea, and applied for forecasting. We considered external features associated with meteorological conditions affecting energy. Two-year training and one-year testing data are preprocessed and arranged to reform the times series, which are then trained in each DL model. The forecasting results of the proposed ensemble model are evaluated by using mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Error metrics are compared with DL stand-alone models such as MLP, CNN, LSTM, and CNN-LSTM. Our ensemble model provides better performance than other forecasting models, providing minimum MAPE at 0.75%, and was proven to be inherently symmetric for forecasting time-series energy and demand data, which is of utmost concern to the power system sector.

Research and Prediction of Yellow Soil Moisture Content in Guizhou Province Based on ARIMA Model

2013 ◽  
Vol 690-693 ◽  
pp. 3076-3081
Author(s):  
Min Liu ◽  
Zhi-Min He
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Arima Model ◽  
Model Fitting ◽  
Absolute Error ◽  
Guizhou Province ◽  
Content Change ◽  
Change Trend

To predict the change trend of guizhou yellow soil moisture content, we employed the ARIMA model of time series, compared the measured data with the prediction data, and the results show that ARIMA time series model fitting soil moisture content change trend is good, predicted value is very close to the observed value. The maximal absolute error, 0.6% and the maximal relative error, 4.2%).The results have practical application value for drought research and management to provide reference[1].

scholarly journals Kalman Filter Algorithm versus Other Methods of Estimating Missing Values: Time Series Evidence

2021 ◽  
Vol 4 (2) ◽  
pp. 1-9
Author(s):  
Adejumo O.A. ◽  
Onifade O.C. ◽  
Albert S.
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Missing Values ◽  
Absolute Error ◽  
Additive Models ◽  
Percentage Error ◽  
Sample Sizes ◽  
Linear Quadratic ◽  
Kalman Filter Algorithm ◽  
Accuracy Measures

Ideally, we think data are carefully collected and have regular patterns with no missing values, but in reality, this does not always happen. This study examines four (4) methods—mean imputation (MI), median imputation (MDI), linear imputation (LI) and Kalman filter algorithm (KAL)—of estimating missing values in time series. The study utilized pairs of nine (9) simulated series; each pair constitutes “actual series” and “12% missingness series”. The three (3) sample sizes i.e. small (50), medium (200) and large (1000) were varied over the additive models linear, quadratic and exponential forms of trend. The 12% missingness series were estimated using MI, MDI, LI and KAL. The performances of the method were checked using the root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), while the overall performances of the estimating methods were accessed using the average of the accuracy measures (RMSE, MAE and MAPE). The results of the average-accuracy measures show that KAL outperformed other methods (MI, MDI and LI) at the three sample sizes when the trend was linear; also, MDI outperformed other methods at the three (3) sample sizes when the trend was exponential. Furthermore, MI outperformed others at small and large sample sizes when the trend was quadratic. However, the Kalman filter algorithm proved better when the sample size was medium. Hence, KAL, MI and MDI methods are recommended to estimate missing data in time series when the trend is linear, quadratic and exponential respectively, until further study proves otherwise.

scholarly journals Crop Yield Prediction Using Multitemporal UAV Data and Spatio-Temporal Deep Learning Models

2020 ◽  
Vol 12 (23) ◽  
pp. 4000
Author(s):  
Petteri Nevavuori ◽  
Nathaniel Narra ◽  
Petri Linna ◽  
Tarmo Lipping
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Crop Yield ◽  
Time Series Data ◽  
Weather Data ◽  
Series Data ◽  
Percentage Error ◽  
Time Series Modelling ◽  
Spatio Temporal ◽  
3D Cnn

Unmanned aerial vehicle (UAV) based remote sensing is gaining momentum worldwide in a variety of agricultural and environmental monitoring and modelling applications. At the same time, the increasing availability of yield monitoring devices in harvesters enables input-target mapping of in-season RGB and crop yield data in a resolution otherwise unattainable by openly availabe satellite sensor systems. Using time series UAV RGB and weather data collected from nine crop fields in Pori, Finland, we evaluated the feasibility of spatio-temporal deep learning architectures in crop yield time series modelling and prediction with RGB time series data. Using Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) networks as spatial and temporal base architectures, we developed and trained CNN-LSTM, convolutional LSTM and 3D-CNN architectures with full 15 week image frame sequences from the whole growing season of 2018. The best performing architecture, the 3D-CNN, was then evaluated with several shorter frame sequence configurations from the beginning of the season. With 3D-CNN, we were able to achieve 218.9 kg/ha mean absolute error (MAE) and 5.51% mean absolute percentage error (MAPE) performance with full length sequences. The best shorter length sequence performance with the same model was 292.8 kg/ha MAE and 7.17% MAPE with four weekly frames from the beginning of the season.

scholarly journals Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform

2019 ◽  
Vol 9 (6) ◽  
pp. 1108 ◽  
Author(s):  
Yao Liu ◽  
Lin Guan ◽  
Chen Hou ◽  
Hua Han ◽  
Zhangjie Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Wind Power ◽  
Short Term Memory ◽  
Wind Farms ◽  
Absolute Error ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Short Term

A wind power short-term forecasting method based on discrete wavelet transform and long short-term memory networks (DWT_LSTM) is proposed. The LSTM network is designed to effectively exhibit the dynamic behavior of the wind power time series. The discrete wavelet transform is introduced to decompose the non-stationary wind power time series into several components which have more stationarity and are easier to predict. Each component is dug by an independent LSTM. The forecasting results of the wind power are obtained by synthesizing the prediction values of all components. The prediction accuracy has been improved by the proposed method, which is validated by the MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean square error) of experimental results of three wind farms as the benchmarks. Wind power forecasting based on the proposed method provides an alternative way to improve the security and stability of the electric power network with the high penetration of wind power.

scholarly journals Evaluation of Several Error Measures Applied to the Sales Forecast System of Chemicals Supply Enterprises

2020 ◽  
Vol 11 (4) ◽  
pp. 39
Author(s):  
Ma. del Rocío Castillo Estrada ◽  
Marco Edgar Gómez Camarillo ◽  
María Eva Sánchez Parraguirre ◽  
Marco Edgar Gómez Castillo ◽  
Efraín Meneses Juárez ◽  
...  
Keyword(s):  
Forecast Model ◽  
Absolute Error ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Error Evaluation ◽  
Sales Forecast ◽  
Absolute Percentage Error ◽  
Sales Prediction ◽  
Prediction Techniques ◽  
Evaluation Techniques

The objective of the industry in general, and of the chemical industry in particular, is to satisfy consumer demand for products and the best way to satisfy it is to forecast future sales and plan its operations.Considering that the choice of the best sales forecast model will largely depend on the accuracy of the selected indicator (Tofallis, 2015), in this work, seven techniques are compared, in order to select the most appropriate, for quantifying the error presented by the sales forecast models. These error evaluation techniques are: Mean Percentage Error (MPE), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Symmetric Mean Absolute Percentage Error (SMAPE) and Mean Absolute Arctangent Percentage Error (MAAPE). Forecasts for chemical product sales, to which error evaluation techniques are applied, are those obtained and reported by Castillo, et. al. (2016 & 2020).The error measuring techniques whose calculation yields adequate and convenient results, for the six prediction techniques handled in this article, as long as its interpretation is intuitive, are SMAPE and MAAPE. In this case, the most adequate technique to measure the error presented by the sales prediction system turned out to be SMAPE.

scholarly journals Forecasting Tourist Arrival in the Province of Surigao del Sur, Philippines using Time Series Analysis

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Ruby Mae Ebuna Maliberan
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Forecast Model ◽  
Mean Absolute Percentage Error ◽  
Actual Data ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Increasing Trend ◽  
The Mean

The study attempted to forecast the number of tourist arrival in the province of Surigao del Sur using the historical monthly tourist arrival data from 2012-2016 using three time series. Findings showed that the tourist arrival in the province is likely to be increasing. As more foreign and local tourist arrivals are expected as a result of forecast model. Furthermore, it showed that there was a long term increasing trend of the tourist arrival in the province. Results revealed that the Mean Absolute Percentage Error (MAPE) of the forecasted tourist arrival data yielded an error of 11 % which means that predicted data is closer to the actual data. Based on the findings of the study, the researcher recommends that this study can be adapted by other Tourism Office of CARAGA, Philippines. 

scholarly journals Fuzzy Time Series for Forecasting Railway Passengers in Indonesia

2021 ◽  
Vol 2111 (1) ◽  
pp. 012013
Author(s):  
Muhammad Fatih Rizqon ◽  
Handaru Jati
Keyword(s):  
Time Series ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Time Series Models ◽  
Suitable Model ◽  
Arithmetic Operations ◽  
Advantages And Disadvantages ◽  
Absolute Percentage Error

Abstract Some fuzzy time series models have their own advantages and disadvantages. In addition, these models sometimes are complex and claimed to have better forecasting result than each other. The suitable model for forecasting depends on a wide variety of considerations. The models proposed by Chen (1996) applied simplified arithmetic operations and claimed more efficiency than before. The model proposed by Chen was introduced in 1996 and still exists in several previous studies. This research aims to forecast the number of railway passengers in Indonesia using the fuzzy time series. In addition, this research also evaluates the forecasting results based on mean absolute error (MAE) and mean absolute percentage error (MAPE). The results showed the forecasting results in this research has accuracy for 86.6%.

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