scholarly journals Comparison Between ARIMA and VAR Model Regarding the Forecasting of the Price of Jute Goods in Bangladesh

2018 ◽  
Vol 66 (2) ◽  
pp. 91-94
Author(s):  
Shahanaj Parvin ◽  
Murshida Khanam
Keyword(s):  
Granger Causality ◽  
Moving Average ◽  
Arima Model ◽  
Var Model ◽  
Data Set ◽  
Augmented Dickey Fuller ◽  
Adf Test

In this study we used Autoregrressive Intigrated Moving Average (ARIMA) and Vector Autoregrressive (VAR) model to analyze and forecast the price of total Jute Goods with four of its types, where data has been collected from Bangladesh Jute Mills Corporation (BJMC) from the year 1980-81 to 2013-2014. In this study, a comparison has been made regarding ARIMA model and VAR model to investigate which model is the best to forecast. The methodology employed in this study is the co-integration and Granger Causality under VECM. The Augmented Dickey Fuller (ADF) Test has been performed to test the stationarity of the data set. The findings of this study suggested that in forecasting the price of jute goods of Bangladesh, the ARIMA model is more efficient than VAR model. Dhaka Univ. J. Sci. 66(2): 91-94, 2018 (July)

scholarly journals Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 349-356
Author(s):  
J. HAZARIKA ◽  
B. PATHAK ◽  
A. N. PATOWARY
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Demand Management ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Data Set ◽  
Modeling And Forecasting ◽  
Moving Average Model

Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points.  We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.  

scholarly journals PRICE INTERACTIONS AND CAUSAL RELATIONSHIPS AMONG CORN, EXCHANGE RATE AND ANIMAL PROTEIN SOURCES IN NIGERIA

2021 ◽  
Vol 59 (1) ◽  
Author(s):  
Unekwu Onuche
Keyword(s):  
Exchange Rate ◽  
Granger Causality ◽  
Poultry Meat ◽  
Causality Test ◽  
Vector Autoregressive ◽  
Meat Price ◽  
Augmented Dickey Fuller ◽  
Causality Tests ◽  
Short Run ◽  
Adf Test

Price transmissions between corn, exchange rate, poultry meat, and fish were investigated using the data from OECD-FAO for the years 1990-2019, to establish the existence of long-term relationships between them and identify their directions of causality, in order to elicit investmentaiding facts. The augmented Dickey-Fuller (ADF) test, the Johansen cointegration approach and the Granger causality test were employed. Following the ADF test, all series are I(1), while the cointegration test indicates short-run dynamics between them. The Vector Autoregressive (VAR) system reveals that poultry meat price influences all variables, prices of poultry meat and exchange rate relate positively to their own lags, and exchange rate relates positively to lags of poultry meat prices. A positive relationship was noticed between fish price and lags of poultry meat price, while corn price relates positively with lags of poultry meat price. Granger causality tests indicate unidirectional drives from poultry price to fish price, the exchange rate to fish price and poultry meat price to corn price. Responses from prices of fish, corn and poultry to innovations from exchange rate are negative, while positive responses exist in other scenarios. Exchange rate stabilization will mitigate external risks, especially to the fisheries sector, while corn farmers can increase profits in the short-run by exploring knowledge of poultry meat price movements.

scholarly journals Forecasting Monthly Gold Prices using ARIMA Model: Evidence from Indian Gold Market

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1373-1376
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Gold Price ◽  
Data Series ◽  
Suitable Model ◽  
Natural Logarithm ◽  
Price Series ◽  
Gold Market ◽  
Sample Data ◽  
Augmented Dickey Fuller

In this paper an attempt has been made to give an overview of the Indian gold market so as to develop a model enabling the forecast of gold prices in India. One troy ounce is equal to 31.103 grams. The monthly sample data of gold price (in INR per troy ounce) is taken from December 1997 to December 2017.The entire data has been divided into two segments for estimation and validation sample and to find out the efficiency and accuracy of forecasting models. Since the gold price data series have shown much deviation after March 2006 the first segment of the data is taken from the time period of December 1997 to March 2006 and second segment from April 2006 to December 2017.Due to a larger value and a huge time span of the sample data, the natural logarithm of gold price has been taken to conduct the study and build an effective model to forecast future gold prices. The unit root tests of Augmented Dickey Fuller‖ and Philips Perron have been used to test the gold price series as stationary or non-stationary. It is observed that series are stationary at first difference in both the methods. At first difference the ACFs and PACFs were pattern less and statistically not significant. Box-Jenkins’s Autoregressive Integrated Moving Average of Box-Jenkins methodology has been used for developing a forecasting model of gold price in India. Different models of ARIMA have been used to obtain best suitable model for forecasting using Eviews software 10 for both time periods i.e., December 1997 to March 2006 & April 2006 to December 2017

scholarly journals Prediction of Area and Production of Groundnut Using Box-Jenkins Arima and Neural Network Approach

2020 ◽  
Author(s):  
S. T. Pavana Kumar ◽  
Ferdinand B. Lyngdoh
Keyword(s):  
Neural Network ◽  
Moving Average ◽  
Arima Model ◽  
Network Models ◽  
Arima Models ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Data Set ◽  
The Neural Network ◽  
Hidden Layer

Selection of parameters for Auto Regressive Integrated Moving Average (ARIMA) model in the prediction process is one of the most important tasks. In the present study, groundnut data was utlised to decide appropriate p, d, q parameters for ARIMA model for the prediction purpose. Firstly, the models were fit to data without splitting into training and validation/testing sets and evaluated for their efficiency in predicting the area and production of groundnut over the years. Meanwhile, models are compared among other fitted ARIMA models with different p, d, q parameters based on decision criteria’s viz., ME, RMSE, MAPE, AIC, BIC and R-Square. The ARIMA model with parameters p-2 d-1-2, q-1-2 are found adequate in predicting the area as well as production of groundnut. The model ARIMA (2, 2, 2) and ARIMA (2,1,1) predicted the area of groundnut crop with minimum error estimates and residual characteristics (ei). The models were fit into split data i.e., training and test data set, but these models’ prediction power (R-Square) declined during testing. In case of predicting the area, ARIMA (2,2,2) was consistent over the split data but it was not consistent while predicting the production over years. Feed-forward neural networks with single hidden layer were fit to complete, training and split data. The neural network models provided better estimates compared to Box-Jenkins ARIMA models. The data was analysed using R-Studio.

scholarly journals NILAI TUKAR RUPIAH DAN NET EKSPOR INDONESIA 2000 – 2017 (GRANGER CAUSALITY TEST)

2020 ◽  
Vol 1 (1) ◽  
pp. 9-24
Author(s):  
Try Beta Anggraini ◽  
Yefriza Yefriza
Keyword(s):  
Exchange Rate ◽  
Granger Causality ◽  
Time Series Data ◽  
Secondary Data ◽  
Granger Causality Test ◽  
Series Data ◽  
Causality Test ◽  
Augmented Dickey Fuller ◽  
Net Export ◽  
Adf Test

The aims of this research is to find out the relationship of rupiah exchange rate and net export Indonesia. This research covers the periode for 2000.Q1-2017.Q4, used secondary data which were analyzed using Granger Causality Test and Augmented Dickey Fuller (ADF) and existing data processed by using computer program of Eviews 9.0. The stationary properties of the time series data are examined by using Augmented Dickey-Fuller (ADF) test. Granger Causality test is applied to find out long-run relationship along with causality among the variables. The result of the data analysis show that there is no causality between rupiah exchange rate and net xport. Granger Causality test showed that there is unidirectional causality between net export to rupiah exchange rate. It is mean that net export  effect rupiah exchange rate, but rupiah exchange rate does not effect net export. Keywords: Causality, Net Export, Exchange Rate

scholarly journals Comparison of the Performance of the SANN, SARIMA and ARIMA Models for Forecasting Quarterly GDP of Nigeria

2021 ◽  
pp. 1-20
Author(s):  
Chukwudike C. Nwokike ◽  
Emmanuel W. Okereke
Keyword(s):  
Mean Squared Error ◽  
Predictive Performance ◽  
Arima Models ◽  
Data Set ◽  
Variance Stabilization ◽  
Augmented Dickey Fuller ◽  
Chi Squared ◽  
Competing Models ◽  
Hidden Layer ◽  
Adf Test

This research aimed at modelling and forecasting the quarterly GDP of Nigeria using the Seasonal Artificial Neural Network (SANN), SARIMA and Box-Jenkins models as well as comparing their predictive performance. The three models mentioned earlier were successfully fitted to the data set. Tentative architecture for the SANN was suggested by varying the number of neurons in the hidden layer while that of the input and output layer remained constant at 4. It was observed that the best architecture was when the hidden layer had 10 neurons and thus SANN (4-10-4) was chosen as the best. In fitting the ARIMA/SARIMA models, the Augmented Dickey Fuller (ADF) test was used to check for stationarity. Variance stabilization and Stationarity were achieved after logarithm transformation and first regular differencing. The ARIMA/SARIMA model with lowest AIC, BIC and HQIC values was chosen as the best amongst the competing models and fitted to the data. The adequacy of the fitted models was confirmed observing the correlogram of the residuals and the Ljung-Box Chi-Squared test result. The SANN model performed better than the SARIMA and ARIMA models as it had a Mean Squared Error value of 0.004 while SARIMA and ARIMA had mean squared errors of 0.527 and 0.705 respectively. It was concluded that the SANN which is a non-linear model be used in modelling the quarterly GDP of Nigeria. Hybrid models which combine the strength of individual models are recommended for further research.

scholarly journals Indo-Pak Comparison Of COVID-19 By ARIMA Model: A Secondary Analysis

2021 ◽  
Author(s):  
Osama Ajaz ◽  
Muhammad Irfan ◽  
Muhammad Amjad ◽  
Ayesha Siddiqa
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Secondary Analysis ◽  
Health Spending ◽  
Climatic Conditions ◽  
World Health ◽  
Data Set ◽  
Related Case ◽  
Time Period ◽  
The World

Abstract Background: Historically, the world has faced and recovered from many pandemics. The most recent global pandemic facing the entire world is Covid-19. India and Pakistan can be considered to be in the same phases of development and health spending relative to their GDP and also have similar climatic conditions. The main aim of the study is to forecast cumulative cases and deaths in Pakistan and India, which will be helpful for policy makers to plan accordingly.Methods: The data set was obtained from the World Health Organization (WHO) website (https://covid19.who.int). The time period we have considered since the first corona related case and death were observed in both countries. The dataset for Pakistan covered the time period from 28th February 2020 to 28th February 2021 and for India 30th January to 28th February 2021. The Auto-Regressive integrated moving average (ARIMA) model was applied for forecasting using R-package.Results: Our results forecasted that cumulative COVID-19 cases at the end of June 2021, at the end of September 2021 and at the end of December 2021 will be 13065792, 14704450, and 16481122 respectively in India while for Pakistan, we forecasted that at the end of June 2021, at the end of September 2021 and at the end of December 2021 will be 746963.5 873557.3and 999766.5 respectively. Cumulative deaths were also forecasted for Pakistan and India. We predicted cumulative deaths as at the end of June 2021 at the end of June 2021, at the end of September 2021 and at the end of December 2021 will be 170586.5, 181153.4 and 192017.5 respectively in India while for Pakistan, we forecasted that cumulative deaths at the end of June 2021, at the end of September 2021 and at the end of December 2021 will be 17890.98, 21825.26 and 25849.4 respectively.Conclusion: Corona related cumulative cases and deaths are on the rise in both countries. The pandemic situation in India is worse than in Pakistan nevertheless both countries are at high risk. There is a sudden increasing pattern in the number of corona related cases in both countries.

scholarly journals Autoregressive Integrated Moving Average (ARIMA-Box Jenkins) Pada Peramalan Komoditas Cabai Merah di Indonesia

2021 ◽  
Vol 5 (2) ◽  
pp. 485
Author(s):  
Ridha Maya Faza Lubis ◽  
Zakarias Situmorang ◽  
Rika Rosnelly
Keyword(s):  
Market Share ◽  
Moving Average ◽  
Central Bureau ◽  
Autoregressive Integrated Moving Average ◽  
Price Fluctuations ◽  
Weight Value ◽  
Augmented Dickey Fuller ◽  
Stationarity Test ◽  
Adf Test ◽  
Selection Of

Chili is one of the main staples in making a dish and chili is one of the values in a commodity that has superior value, the price of chili often experiences price fluctuations or what is known as the price which is always changing. data taken from BPS (Central Bureau of Statistics) data nationally from January 2001 to December 2015 data, this study also aims to be able to predict national chili prices which will later be used in research, namely discussing the Autoregressive Integrated Moving Average (ARIMA) method. In this study, the identification of the model was carried out using two tests, namely the stationarity test and the correlation test. The stationarity test is the Augmented Dickey-Fuller (ADF) test, the Philips-Perron (PP) test and the Kwiatkowski-Philips-Schmidt-Shin (KPPS) test using Minitab 9.The chili commodity is a very important commodity in the Indonesian economy, because In terms of consumption, chilies have a very significant market share, which can be seen from data from the Central Statistics Agency (BPS) with an inflation weight value of 0.35%. From the research, it was found that for the selection of the best method, namely ARIMA (3,1,0) because it has the smallest MSE value and the forecasting results for the next 12 periods in January 2016 ranged from Rp. 11,868.2 to Rp. 28,315.5 and so on until December 2016.

scholarly journals S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Madhavi Latha Challa ◽  
Venkataramanaiah Malepati ◽  
Siva Nageswara Rao Kolusu
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Descriptive Statistics ◽  
Autoregressive Integrated Moving Average ◽  
Augmented Dickey Fuller ◽  
The Mean ◽  
Daily Stock Returns

AbstractThis study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange. To achieve the objectives, the study uses descriptive statistics; tests including variance ratio, Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski Phillips Schmidt and Shin; and Autoregressive Integrated Moving Average (ARIMA). The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series, using the ARIMA model. The results reveal that the mean returns of both indices are positive but near zero. This is indicative of a regressive tendency in the long-term. The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values, with few deviations. Hence, the ARIMA model is capable of predicting medium- or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

scholarly journals FORECASTING DROUGHT WITH ARIMA MODEL AND STANDARDIZED PRECIPITATION INDEX (SPI)

2019 ◽  
Vol 1 (2) ◽  
pp. 32-34
Author(s):  
ALFA MOHAMMED SALISU
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Data Series ◽  
Drought Forecasting ◽  
Data Set ◽  
Forecasting Accuracy ◽  
Diagnostic Checking ◽  
Auto Regressive

Drought forecasting is an important forecasting procedure for preparing and managing water resources for all creatures. Natural disasters across the regions such as flooding, earthquakes, droughts etc. have caused damages to life as a result of which numerous researches have been conducted to assist in reducing the phenomenon. Consequently, therefore, this study considered using Auto-Regressive Integrated Moving Average (ARIMA) model in forecasting drought using Standardized Precipitation Index (SPI) as a forecasting tool which was used to measure and classify drought. The models are developed to forecast the SPI series. Results indicated the forecasting ability of the ARIMA models which increases as the timescales. The study is aimed at using ARIMA method for modeling SPI data series. The studies used data set made up of 624 months, obtained from 1954 to 2008. In the analysis only SPI3 series was non-seasonal while others have seasonality and Seasonal ARIMA was carried out, SPI12 was significant compared with the forecasting accuracy alongside the diagnostic checking having a minimum error of RMSE and MAE in both testing and training phases. The research contributes to the discovering of feasible forecasting of drought and demonstrates that the established model is good and appropriate for forecasting drought.

Sign in / Sign up

Export Citation Format

Share Document