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 Using ARIMA Models to Forecasting of Economic Variables of Wheat Crop in Afghanistan

2020 ◽  
pp. 1-21
Author(s):  
M. Karim Ahmadzai ◽  
Moataz Eliw
Keyword(s):  
Time Series ◽  
Correlation Function ◽  
Time Series Analysis ◽  
Partial Correlation ◽  
Wheat Crop ◽  
Prediction Methods ◽  
Arima Models ◽  
Self Sufficiency ◽  
Univariate Time Series ◽  
Production And Consumption

Wheat is considered the main food crops in Afghanistan, whether to use it for majority of the population consumption or to use it in some industries and others. Problem: Afghanistan suffers from a large gap between production and consumption, so the current research investigates the problem arising from a shortage of wheat production to meet self-sufficiency of the population. Methods: The time series analysis can provide short-run forecast for sufficiently large amount of data on the concerned variables very precisely. In univariate time series analysis, the ARIMA models are flexible and widely used. The ARIMA model is the combination of three processes: (i) Autoregressive (AR) process, (ii) Differencing process and (iii) Moving-Average (MA) process. These processes are known in statistical literature as main univariate time series models and are commonly used in many applications. Where, Estimation of future wheat requirement is one of the essential tools that may help decision-makers to determine wheat needs and then developing plans that help reduce the gap between production and consumption. A solid strategy that widely applying of improved seeds and fertilizers, an effective research and extension system for better crop management is necessary to eliminate this gap for self-sufficiency in wheat production, besides providing the necessary financial sums for that. Where most prediction methods are valid for one-year prediction. However, moving prediction methods have been found to measure and predict the future movement of the dependent variable. Aims: The current research aims to prediction for Area, Productivity, Production, Consumption and Population over the period (2002-2017), to estimate the values of these variables in the period of (2018-2030). Results: The results showed that through the drawing of the historical data for Planted area, Productivity, Production, Consumption and Population of wheat crop it was evident that the series data is not static due to an increasing or a decreasing of general trend, which means the instability of the average, by using Auto-correlation function (ACF) and Partial Correlation Function to detect the stability of the time series, The results showed also, the significance of Autocorrelation coefficient and partial correlation coefficient values, which indicates that the time series is not static.

scholarly journals Lithuanian electricity market price forecasting model based on univariate time series analysis

Energetika ◽  
2020 ◽  
Vol 66 (1) ◽  
Author(s):  
Mindaugas Česnavičius
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Electricity Market ◽  
Statistical Tests ◽  
Arima Model ◽  
Electricity Price ◽  
Data Set ◽  
Forecasting Models ◽  
Series Analysis ◽  
Univariate Time Series

Electricity price changes can significantly affect expenses in energy intensive industries, adjust profits or losses for electricity retailers and cause problems for country’s national energy strategy implementation. Forecasting models based on statistical methods and previous variable values help to predict future values and adjust strategy according to the forecast. This paper concentrates on the Lithuanian electricity market and presents the widely used ARIMA forecasting models based on the univariate time series analysis. The Lithuanian electricity market is selected due to a lack of statistical researches based on electricity market prices in Lithuania, as well as significant future electricity market liberalization projects. Electricity price data for analysis are taken from the Nord Pool electricity market operator website. The Nord Pool represents the Northern Europe electricity market operator where Lithuania and other 14 European countries trade electricity on a daily basis. To provide a long-term electricity price outlook average monthly data from July 2012 to December 2019 are selected for analysis. Before building the ARIMA model data are tested with various statistical tests to guarantee that time series are stationary, there is no autocorrelation or structural breaks. Once the data validity is confirmed, the time series is divided into train and test sets. The train data set is used to create a fitting ARIMA model, while the test set is used to define forecasting accuracy. Created forecasts of models are compared between each other using common comparison statistics, and the most accurate models are defined. Finally, the selected model is trained on a full dataset and the electricity price forecast for the year 2020 is constructed. The created AR (1) model had the smallest error value compared to the test dataset, while the SARIMA (1,1,1) model had the best approximation statistics. By combining both models the weighted SARIMA (1,1,1) model is constructed with the features of low forecasting error and precise actual time series approximation. The final model forecast for the year 2020 shows the monthly average electricity price decrease at the beginning of the year, a significant increase at the second half of the year and a price drop at the end of the year. Forecasting results can help companies to plan their electricity production and maintenance periods to maximize income from sold energy and minimize potential losses due to planned shutdown.

Recent Mortality Trends in the Spanish Population

2002 ◽  
Vol 8 (4) ◽  
pp. 757-786 ◽  
Author(s):  
A. Felipe ◽  
M. Guillen ◽  
A. M. Perez-Marin
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Spanish Population ◽  
Mortality Trends ◽  
Calendar Time ◽  
Series Analysis ◽  
Univariate Time Series ◽  
Using Data ◽  
The Way

ABSTRACTOur research deals with the way that calendar time affects mortality patterns in the Spanish population, and how this information can be used to elaborate predictions. A description of the observed mortality evolution has been worked out using data from 1975 to 1993. We have used Heligman-Pollard Law number two to model the evolution of Spanish mortality over the period and using univariate time series analysis, we have obtained a prognosis for years 1994 to 2010.

Predicting Daily Confirmed COVID-19 Cases in India

2021 ◽  
pp. 34-51
Author(s):  
Sudip Singh
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Family Welfare ◽  
Mean Square ◽  
Univariate Time Series ◽  
Partial Autocorrelation Function ◽  
Auto Regressive

India, with a population of over 1.38 billion, is facing high number of daily COVID-19 confirmed cases. In this chapter, the authors have applied ARIMA model (auto-regressive integrated moving average) to predict daily confirmed COVID-19 cases in India. Detailed univariate time series analysis was conducted on daily confirmed data from 19.03.2020 to 28.07.2020, and the predictions from the model were satisfactory with root mean square error (RSME) of 7,103. Data for this study was obtained from various reliable sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/. The model identified was ARIMA(1,1,1) based on time series decomposition, autocorrelation function (ACF), and partial autocorrelation function (PACF).

scholarly journals A Fuzzy Set-Valued Autoregressive Moving Average Model and Its Applications

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 324 ◽  
Author(s):  
Dabuxilatu Wang ◽  
Liang Zhang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Analysis ◽  
Moving Average ◽  
Arma Model ◽  
Autoregressive Moving Average ◽  
Complex Data ◽  
Arma Models ◽  
Linguistic Data ◽  
Series Analysis

Autoregressive moving average (ARMA) models are important in many fields and applications, although they are most widely applied in time series analysis. Expanding the ARMA models to the case of various complex data is arguably one of the more challenging problems in time series analysis and mathematical statistics. In this study, we extended the ARMA model to the case of linguistic data that can be modeled by some symmetric fuzzy sets, and where the relations between the linguistic data of the time series can be considered as the ordinary stochastic correlation rather than fuzzy logical relations. Therefore, the concepts of set-valued or interval-valued random variables can be employed, and the notions of Aumann expectation, Fréchet variance, and covariance, as well as standardized process, were used to construct the ARMA model. We firstly determined that the estimators from the least square estimation of the ARMA (1,1) model under some L2 distance between two sets are weakly consistent. Moreover, the justified linguistic data-valued ARMA model was applied to forecast the linguistic monthly Hang Seng Index (HSI) as an empirical analysis. The obtained results from the empirical analysis indicate that the accuracy of the prediction produced from the proposed model is better than that produced from the classical one-order, two-order, three-order autoregressive (AR(1), AR(2), AR(3)) models, as well as the (1,1)-order autoregressive moving average (ARMA(1,1)) model.

Time Series Analysis to Monitor and Assess Water Resources: A Moving Average Approach

2005 ◽  
Vol 109 (1-3) ◽  
pp. 65-72 ◽  
Author(s):  
Rajesh Reghunath ◽  
T. R. Sreedhara Murthy ◽  
B. R. Raghavan
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Time Series Analysis ◽  
Moving Average ◽  
Series Analysis
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