scholarly journals Forecasting of boro rice production in Bangladesh: An ARIMA approach

1970 ◽  
Vol 8 (1) ◽  
pp. 103-112 ◽  
Author(s):  
NMF Rahman
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Rice Production ◽  
Arima Models ◽  
Short Term ◽  
Production Uncertainty ◽  
Future Production ◽  
Short Run ◽  
The Government ◽  
Boro Rice

The study was undertaken to examine the best fitted ARIMA model that could be used to make efficient forecast boro rice production in Bangladesh from 2008-09 to 2012-13. It appeared from the study that local, modern and total boro time series are 1st order homogenous stationary. It is found from the study that the ARIMA (0,1,0) ARIMA (0,1,3) and ARIMA (0,1,2) are the best for local, modern and total boro rice production respectively. It is observed from the analysis that short term forecasts are more efficient for ARIMA models. The production uncertainty of boro rice can be minimizing if production can be forecasted well and necessary steps can be taken against losses. The government and producer as well use ARIMA methods to forecast future production more accurately in the short run. Keywords: Production; ARIMA model; Forecasting. DOI: 10.3329/jbau.v8i1.6406J. Bangladesh Agril. Univ. 8(1): 103-112, 2010

scholarly journals Rice Production In Bangladesh Employing By Arima Model

1970 ◽  
Vol 36 (1) ◽  
pp. 51-62 ◽  
Author(s):  
MA Awal ◽  
MAB Siddique
Keyword(s):  
Time Series Data ◽  
Arima Model ◽  
Rice Production ◽  
Series Data ◽  
Arima Models ◽  
Short Term ◽  
Deterministic Models ◽  
Policy Makers ◽  
Short Run ◽  
Boro Rice

The present study was carried out to estimate growth pattern and also examine the best ARIMA model to efficiently forecasting Aus, Aman and Boro rice production in Bangladesh. It appeared that the time series data for Aus and Aman were 1st order homogenous stationary but Boro was 2nd order stationary. The study revealed that the best models were ARIMA (4,1,4), ARIMA (2,1,1), and ARIMA (2,2,3) for Aus, Aman, and Boro rice production, respectively. The analysis indicated that short-term forecasts were more efficient for ARIMA models compared to the deterministic models. The production uncertainty of rice could be minimized if production were forecasted well and necessary steps were taken against losses. The findings of this study would be more useful for policy makers, researchers as well as producers in order to forecast future national rice production more accurately in the short run.   Keywords: Production; ARIMA model; forecasting of rice. DOI: http://dx.doi.org/10.3329/bjar.v36i1.9229 BJAR 2011; 36(1): 51-62

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.

scholarly journals Forecasting the Prevalence of COVID-19 in Maharashtra, Delhi, Kerala, and India using an ARIMA model

2020 ◽  
Author(s):  
Strong P Marbaniang
Keyword(s):  
Time Series ◽  
Performance Measures ◽  
Arima Model ◽  
Arima Models ◽  
Wuhan City ◽  
Policy Interventions ◽  
Time Series Analyses ◽  
The Government ◽  
The Impact ◽  
Best Fit

Abstract Aims: As the whole world was preparing to welcome the year 2020, a new deadly virus, COVID-19, was reported in the Wuhan city of China in late December 2019. By May 18, 2020, approximately 4.7 million cases and 0.32 million deaths had been reported globally. There is an urgent need to predict the COVID-19 prevalence to control the spread of the virus.Methods: Time-series analyses can help understand the impact of the COVID-19 epidemic and take appropriate measures to curb the spread of the disease. In this study, an ARIMA model was developed to predict the trend of COVID-19 prevalence in the states of Maharashtra, Delhi and Kerala, and India as a whole.Results: The prevalence of COVID-19 from 16 March 2020 to 17 May 2020 was collected from the website of Covid19india. Several ARIMA models were generated along with the performance measures. ARIMA (2,3,1), ARIMA (2,2,0), ARIMA (2,2,0), and ARIMA (1,3,1) with the lowest MAPE (5.430, 10.440, 2.607, and 2.390) for Maharashtra, Delhi, Kerala, and India were selected as the best fit models respectively. The findings show that over the next 20 days, the total number of confirmed COVID-19 cases may increase to 2.45 lakhs in India, 93,709 in Maharashtra, 19,847 in Delhi, and 925 in Kerala.Conclusion: The results of this study can throw light on the intensity of the epidemic in the future and will help the government administrations in Maharashtra, Delhi, and Kerala to formulate effective measures and policy interventions to curb the virus in the coming days.

scholarly journals Hybrid Model for Time Series of Complex Structure with ARIMA Components

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1122
Author(s):  
Oksana Mandrikova ◽  
Nadezhda Fetisova ◽  
Yuriy Polozov
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Complex Structure ◽  
Arima Model ◽  
Ionospheric Parameter ◽  
Arima Models ◽  
Complicated Structure ◽  
Threshold Functions ◽  
Anomalous Component

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

scholarly journals A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3299
Author(s):  
Ashish Shrestha ◽  
Bishal Ghimire ◽  
Francisco Gonzalez-Longatt
Keyword(s):  
Time Series ◽  
Kinetic Energy ◽  
Power System ◽  
Bayesian Model ◽  
Arima Model ◽  
Power Converter ◽  
Series Data ◽  
Percentage Error ◽  
Optimal Size ◽  
Short Term

Withthe massive penetration of electronic power converter (EPC)-based technologies, numerous issues are being noticed in the modern power system that may directly affect system dynamics and operational security. The estimation of system performance parameters is especially important for transmission system operators (TSOs) in order to operate a power system securely. This paper presents a Bayesian model to forecast short-term kinetic energy time series data for a power system, which can thus help TSOs to operate a respective power system securely. A Markov chain Monte Carlo (MCMC) method used as a No-U-Turn sampler and Stan’s limited-memory Broyden–Fletcher–Goldfarb–Shanno (LM-BFGS) algorithm is used as the optimization method here. The concept of decomposable time series modeling is adopted to analyze the seasonal characteristics of datasets, and numerous performance measurement matrices are used for model validation. Besides, an autoregressive integrated moving average (ARIMA) model is used to compare the results of the presented model. At last, the optimal size of the training dataset is identified, which is required to forecast the 30-min values of the kinetic energy with a low error. In this study, one-year univariate data (1-min resolution) for the integrated Nordic power system (INPS) are used to forecast the kinetic energy for sequences of 30 min (i.e., short-term sequences). Performance evaluation metrics such as the root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE) of the proposed model are calculated here to be 4.67, 3.865, 0.048, and 8.15, respectively. In addition, the performance matrices can be improved by up to 3.28, 2.67, 0.034, and 5.62, respectively, by increasing MCMC sampling. Similarly, 180.5 h of historic data is sufficient to forecast short-term results for the case study here with an accuracy of 1.54504 for the RMSE.

scholarly journals Do Erratic Rainfalls Hamper Grain Production? Analysis of Supply Response of Rice to Price and Non-Price Factors

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1463
Author(s):  
Ghulam Mustafa ◽  
Azhar Abbas ◽  
Bader Alhafi Alotaibi ◽  
Fahd O. Aldosri
Keyword(s):  
Unit Root Test ◽  
Rice Production ◽  
Vector Error Correction ◽  
Supply Response ◽  
Target Time ◽  
Vector Error ◽  
Long Run ◽  
Short Run ◽  
The Government ◽  
The Impact

Increasing rice production has become one of the ultimate goals for South Asian countries. The yield and area under rice production are also facing threats due to the consequences of climate change such as erratic rainfall and seasonal variation. Thus, the main aim of this work was to find out the supply response of rice in Malaysia in relation to both price and non-price factors. To achieve this target, time series analysis was conducted on data from 1970 to 2014 using cointegration, unit root test, and the vector error correction model. The results showed that the planted area and rainfall have a significant effect on rice production; however, the magnitude of the impact of rainfall is less conspicuous for off-season (season 2) rice as compared to main-season rice (season 1). The speed of adjustment from short-run to long-run for season-1 rice production is almost two-and-a-half years (five production seasons), while for season-2 production, it is only about one-and-a-half year (three production seasons). Consequently, the study findings imply the supply of water to be enhanced through better water infrastructure for both seasons. Moreover, the area under season 2 is continuously declining to the point where the government has to make sure that farmers are able to cultivate the same area for rice production by providing uninterrupted supply of critical inputs, particularly water, seed and fertilizers.

scholarly journals Forecasting of Economic Indicators (Production, Consumption, Population) of Wheat Crop (A Case Study)

2021 ◽  
Vol 8 (6) ◽  
pp. 38-46
Author(s):  
Mohammad Karim Ahmadzai
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Wheat Crop ◽  
Series Analysis ◽  
The People ◽  
Self Sufficiency ◽  
Univariate Time Series ◽  
Short Run

Wheat is the most important food crop in Afghanistan, whether consumed by the bulk of the people or used in various sectors. The problem is that Afghanistan has a significant shortfall of wheat between domestic production and consumption. Thus, the present study looks at the issue of meeting self-sufficiency for the whole population due to wheat shortages. To do so, we employ time series analysis, which can produce a highly exact short-run prediction for a significant quantity of data on the variables in question. The ARIMA models are versatile and widely utilised in univariate time series analysis. The ARIMA model combines three processes: I the auto-regressive (AR) process, (ii) the differencing process, and (iii) the moving average (MA) process. These processes are referred to as primary univariate time series models in statistical literature and are widely employed in various applications. Where predicting future wheat requirements is one of the most important tools that decision-makers may use to assess wheat requirements and then design measures to close the gap between supply and consumption. The present study seeks to forecast Production, Consumption, and Population for the period 2002-2017 and estimate the values of these variables between 2002 and 2017. (2018-2030).  

scholarly journals Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yihuai Huang ◽  
Chao Xu ◽  
Mengzhong Ji ◽  
Wei Xiang ◽  
Da He
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Adaptive Filtering ◽  
Time Series Data ◽  
Arima Model ◽  
Series Data ◽  
Short Term ◽  
Hybrid Prediction ◽  
Service Demand ◽  
Filtering Method

Abstract Background Accurate forecasting of medical service demand is beneficial for the reasonable healthcare resource planning and allocation. The daily outpatient volume is characterized by randomness, periodicity and trend, and the time series methods, like ARIMA are often used for short-term outpatient visits forecasting. Therefore, to further enlarge the prediction horizon and improve the prediction accuracy, a hybrid prediction model integrating ARIMA and self-adaptive filtering method is proposed. Methods The ARIMA model is first used to identify the features like cyclicity and trend of the time series data and to estimate the model parameters. The parameters are then adjusted by the steepest descent algorithm in the adaptive filtering method to reduce the prediction error. The hybrid model is validated and compared with traditional ARIMA by several test sets from the Time Series Data Library (TSDL), a weekly emergency department (ED) visit case from literature study, and the real cases of prenatal examinations and B-ultrasounds in a maternal and child health care center (MCHCC) in Ningbo. Results For TSDL cases the prediction accuracy of the hybrid prediction is improved by 80–99% compared with the ARIMA model. For the weekly ED visit case, the forecasting results of the hybrid model are better than those of both traditional ARIMA and ANN model, and similar to the ANN combined data decomposition model mentioned in the literature. For the actual data of MCHCC in Ningbo, the MAPE predicted by the ARIMA model in the two departments was 18.53 and 27.69%, respectively, and the hybrid models were 2.79 and 1.25%, respectively. Conclusions The hybrid prediction model outperforms the traditional ARIMA model in both accurate predicting result with smaller average relative error and the applicability for short-term and medium-term prediction.

Short-term wind speed forecasting using ARIMA model

Energetika ◽  
2016 ◽  
Vol 62 (1-2) ◽  
Author(s):  
Ernesta Grigonytė ◽  
Eglė Butkevičiūtė
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Moving Average ◽  
Arima Model ◽  
Model Structure ◽  
Optimal Model ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Auto Correlation ◽  
Auto Correlation Function

The massive integration of wind power into the power system increasingly calls for better short-term wind speed forecasting which helps transmission system operators to balance the power systems with less reserve capacities. The  time series analysis methods are often used to analyze the  wind speed variability. The  time series are defined as a sequence of observations ordered in time. Statistical methods described in this paper are based on the prediction of future wind speed data depending on the historical observations. This allows us to find a sufficiently good model for the wind speed prediction. The paper addresses a short-term wind speed forecasting ARIMA (Autoregressive Integrated Moving Average) model. This method was applied for a number of different prediction problems, including the short term wind speed forecasts. It is seen as an early time series methodology with well-known limitations in wind speed forecasting, mainly because of insufficient accuracies of the hourly forecasts for the second half of the day-ahead forecasting period. The authors attempt to find the maximum effectiveness of the model aiming to find: (1) how the identification of the optimal model structure improves the forecasting results and (2) what accuracy increase can be gained by reidentification of the structure for a new wind weather season. Both historical and synthetic wind speed data representing the sample locality in the Baltic region were used to run the model. The model structure is defined by rows p, d, q and length of retrospective data period. The structure parameters p (Autoregressive component, AR) and q (Moving Average component, MA) were determined by the Partial Auto-Correlation Function (PACF) and Auto-Correlation Function (ACF), respectively. The model’s forecasting accuracy is based on the root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE). The results allowed to establish the optimal model structure and the length of the input/retrospective period. The  quantitative study revealed that identification of the  optimal model structure gives significant accuracy improvement against casual structures for 6–8 h forecast lead time, but a season-specific structure is not appropriate for the entire year period. Based on the conducted calculations, we propose to couple the ARIMA model with any more effective method into a hybrid model.

scholarly journals Research on Short-term Ionospheric Prediction Combining with EOF and ARIMA Model Over Guangxi Area

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1147-1153
Author(s):  
C. Li ◽  
H. Peng ◽  
L. K. Huang ◽  
L. L. Liu ◽  
S. F. Xie
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Prediction Accuracy ◽  
Arima Model ◽  
Stationary Time Series ◽  
Short Term ◽  
Equatorial Anomaly ◽  
Average Standard Deviation ◽  
Stationary Time ◽  
Low Latitudes

Abstract. According to the empirical orthogonal function (EOF), the non-stationary time series data are decomposed into time function and space function, so this mathematical method can simplify the non-stationary time series and eliminate redundant information, thus it performs well in non-stationary time series analysis. The ionospheric Vertical Total Electron Content (VTEC) is a non-stationary time series, which has non-stationary and seasonal variation and the activity of VTEC is more active in low latitudes. Guangxi is located in the middle and low latitudes of the Northern Hemisphere with abundant sunshine in summer and autumn. The energy released by solar radiation makes the ionospheric activity in this region more complex than that in the high latitudes. However, no expert or scholar has used EOF analysis method to conduct a comprehensive study of the low latitudes. The International GNSS Service (IGS) provided by high precision Global Ionospheric Maps (GIM) center in Guangxi are used in the modeling data, the GIM data of the first 10 days of different seasons are decomposed by EOF, and then the time function is predicted by ARIMA model. VTEC values for the next five days are obtained through reconstruction, and relative accuracy and standard deviation are used as accuracy evaluation criteria. The results of EOF-ARIMA model are compared with those of ARIMA model, and the prediction accuracy of EOF-ARIMA model at the equatorial anomaly is analyzed in order to explore the reliability of the model in the more complex region of ionospheric activity. The results show that the average relative precision of EOF-ARIMA model is 84.0, the average standard deviation is 7.45TECu, the average relative precision of ARIMA model is 81.5, the average standard deviation is 8.29TECu, and the precision of EOF-ARIMA model is higher than that of ARIMA model.; There is no significant seasonal difference in the prediction accuracy of EOF-ARIMA model, and the prediction accuracy of ARIMA model in autumn is lower than that of other seasons, which indicates that the prediction results of EOF-ARIMA model are more reliable; The prediction accuracy of the EOF-ARIMA model at the equatorial anomaly is not affected, and it is consistent with the accuracy of the high latitude area in Guangxi. It is shown that the EOF-ARIMA model has high accuracy and stability in the short-term ionospheric prediction in Guangxi at low latitudes of China, and provides a new and reliable method for ionospheric prediction at low latitudes.

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