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Abstract
The aim of this paper is to use the time series modeling to research the current trend in the export and import of sheep and goat meat from the world and forecast the same for the next 20 years. The Autoregressive Integrated Moving Average (ARIMA) models were fitted using data records from 1988-2016 to 2017-2036 for the annual sheep and goat meat export and import (quantity) from the globe. Using the fit criterion for goodness, i.e. The Akaike Information Criterion (AIC) was considered to be the most suitable model for export: (3,3,1), (2,2,2) and import: (1,3,2) and (2,2,1) versions. The best model with higher precision was used to predict future annual meat exports and import volumes in the leading 20 years. Annual sheep and goat meat exports and imports from the world in the leading 20 years are predicted to have a growing trend with a positive growth rate observed.

 

2. Materials and Methods

2.1.  The Dataset
In our study, we used the Supply chain dataset [1] to predict goat and sheep trade flow using global trade data. In our dataset have 9 columns for our predictive model. We considered year and weight in kg columns to do time series analysis using ARIMA msodel. Table 1 shows the original attributes from the dataset which is collected from Supply chain data.

 

2.2. ARIMA Model for forecast
Using Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), Degree of Difference and Appropriate Autoregressive and Moving Average Terms are calculated. Generally, the ARIMA parameters were calculated according to the orders considered in the model. The values of p, d and q in the model and their statistical significance were evaluated by t-test. The final model was used to estimate meat exports over the last five years. Four ARIMA models, for example: Model (0, 0, 2); Model (2, 1, 0), Model (4, 0, 0) and Model (1, 4, 0) were evaluated in this study for modeling time series data on sheep and goat meat exports/imports from worldwide, and the best model was subsequently used for forecasting purposes.
All statistical analyzes were performed using the Forecast command in R programming and R studio.

 

2.3. Data Pre-Processing
Data cleaning is the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or incorrectly formatted. Usually, this data is not necessary or helpful when it comes to analyzing the data because it may hinder the process or give incorrect results. There are several methods of cleaning data, depending on how they are stored along with the answers that are being sought. Data cleaning is not simply a matter of erasing information to make room for new data, but rather of finding a way to maximize the accuracy of the data set without necessarily deleting information. 
We find out the all-missing value in our dataset and replace it. We found that in our Data 50789 missing value. In this study we use the method of replacing missing value with KNN. Even though we computing models NN, SVM, NB and RF are very powerful for handling nonlinear, noisy, and non-stationary data, it is possible to improve the performance of models with appropriate data preparation beforehand. Preprocessing data will ensure equal attention is paid to all variables during the training. Data preprocessing can also be a requirement to increase the quality of training. Techniques for data preprocessing include filtering of signals, transformation of data, rescaling or standardization.

 

3. Result and Discussion
In this study, the trend analysis was used to estimate the quantity of sheep and goat exports and imports from a global perspective. For the years 1988-2016 to 2017-2036, different types of trends were used for the export of sheep and goat data. For trend studies and prediction purposes, linear, quadratic and exponential trends have been used. The exponential trend has a minimum of MSE and RMSE among the various trends appropriate for export and import quantity (q), which indicates that the exponential trend was also not best suited for this data.

 

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  • Uploaded By : Brett
  • Posted on : March 08th, 2019
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