Data Driven Approach for Modeling and Forecasting of Maize Yield in India | Chapter 14 | Agricultural Sciences: Techniques and Innovations Vol. 4

 

The demand for food grain crops is increasing at a rapid rate worldwide. To meet the global food demand, efforts are needed for the enhancement of crop yield through improved varieties, policy support, subsidies, resource allocation, market development, and farmers’ motivation towards the cultivation of profitable crops. However, the yield of agricultural crops is influenced by several extraneous factors like climate change, pest attacks, resource scarcity, and land acquisition for construction and urbanisation. This study deals with modelling and forecasting of maize yield in India. The study encompasses temporal data from 1954 to 2023, allowing for a data-driven analysis of trends in maize yield over the years in India. The methodology involved the fitting of several conventional autoregressive integrated moving average (ARIMA) models. The accuracy of the fitted models was evaluated using model fit statistics criteria viz., akaike information criterion (AIC), root mean square error (RMSE) and mean absolute percentage error (MAPE). The best-fitted model was ARIMA (2,1,0) with drift, having an AIC value of 894.95, an RMSE value of 148.05, and an MAPE value of 7.71%. Furthermore, a comparative assessment of the conventional fitted models was made with the automated model, viz., ARIMA (1,1,2) with drift, which was o.btain.d on using function in R-studio. The diagnostic checking of residuals of the generated models was made using the Ljung-Box test. It was revealed that the Ljung-Box test statistic (Q) achieved a p-value greater than 0.05 for residuals of each fitted model, which indicated the acceptance of the null hypothesis (H0), i.e., the residuals of the various generated models were uncorrelated. The analytical results revealed that ARIMA (1,1,2) with a drift model slightly outperformed ARIMA (2,1,0) with a drift. The forecast values of maize yield for five successive years (viz., 2024-2028) were obtained with 80% and 95% prediction intervals utilising ARIMA (1,1,2) with drift model. The outcomes of the analysis reported a significantly rising trend of maize yield over the recent years, which indicated a favourable sign for policymakers and scientists regarding the formulation of strategies related to agricultural trade and nutritional security.

 

 

Author(s) Details

Manish Kumar
Department of Agricultural Statistics, Acharya Narendra Deva University of Agriculture & Technology, Ayodhya, India.

 

Shiv Kumar Rana
Department of Agricultural Statistics, Acharya Narendra Deva University of Agriculture & Technology, Ayodhya, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/asti/v4/6379

Forecasting Air Traffic for Indigo Airlines: An ARFIMA Approach | Chapter 8 | Science and Technology: Developments and Applications Vol. 9

Most of the time the nature of the traffic is bursty. This traffic congestion may be studied mathematically as LRD (long-range dependence) or self-similarity. The subject of the analysis is Indigo with the number of passengers travelled by Domestic flights being the focus. The analysis utilized time series data on the number of passengers traveling on Air India domestic flights from January 2012 to December 2018. The data has been taken from the Directorate General of Civil Aviation (DGCA) website. SPSS and STATA software were used for this analysis. This study fitted the time series models ARIMA and ARFIMA (or FARIMA) to flight Indian domestic flight passenger data, which exhibit self-similarity and Long Range Dependence (LRD). In this scenario, the ARFIMA model is anticipated to outperform the ARIMA model. ARIMA and ARFIMA models were applied to air traffic data and a comparison was conducted. The optimal model has been determined utilizing RMSE, MAE, and MAPE metrics. This model can effectively assess air traffic flow and optimize Indigo's services. The results show that RMSE, MAE and MAPE are lower for the ARFIMA model than ARIMA. Also, the R-squared value is greater for the ARFIMA model than ARIMA, which shows ARFIMA model is superior to ARIMA for this data. In conclusion, predictions regarding the future air traffic of Indigo domestic carriers may be made with the help of the ARFIMA model as it is beneficial for Indigo to adjust their services with the help of this study.

 

Author (s) Details

Manohar Dingari
Anurag University, Hyderabad, India.

 

V. Sumalatha
Veeranari Chakari Ilamma Women’s University, Koti, Hyderabad, India.

 

S. Hariprasad
Anurag University, Hyderabad, India.

 

S. Lavanya
Visvesvaraya College of Engineering and Technology, Hyderabad, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v9/5117

Application of Forecasting Model to Study the Population Growth of India | Chapter 4 | Mathematics and Computer Science: Research Updates Vol. 4

This study aims to compare the forecasting accuracy of three-time series models—Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing State Space Model (ETS), and State-Space Model with Kalman Filtering—for predicting India’s population trends from 2021 to 2091.

Study Design: A comparative analysis of time series forecasting models based on historical population data.

Methodology: The study employs statistical and machine learning forecasting models: ARIMA, ETS, and Kalman Filtering. These models were applied to obtain forecasts and compared based on their errors and alignment with currently available data.

Results: The ARIMA model estimates a decline in India's population after 2051, while the ETS and Kalman Filtering models suggest continuous growth until 2091. The ARIMA model shows the lowest error rate (MAE: 14.63, RMSE: 24.24, MAPE: 3.36%), providing better short-term accuracy. The ETS model offers a more reliable long-term projection, though with slightly higher errors. The Kalman Filtering model presents the highest error values (MAE: 42, RMSE: 54.06, MAPE: 12.67%), reflecting greater uncertainty in its estimates.

Conclusion: Among the statistical models, ARIMA delivers the most accurate short-term forecasts, while ETS and Kalman Filtering are more suitable for long-term projections. In the machine learning forecast, XGBoost was found to be the most accurate for long-term population forecasting. The study highlights the strengths and limitations of each model and underscores the importance of selecting an appropriate forecasting method based on the required time horizon and accuracy needs.

 

Author (s) Details

 

Abhishek Pandey
Department of Computer Science and Engineering, Institute of Advanced Research, The University for Innovation, Gandhinagar, Gujarat. Pin :382426, India.

 

Sanjay Sonar
Department of Computer Science and Engineering, Institute of Advanced Research, The University for Innovation, Gandhinagar, Gujarat. Pin :382426, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcsru/v4/4778

Determining the Causal Effect of World Cocoa Price on Production of Cocoa in Ghana: A Modeling Approach | Chapter 7 | Emerging Issues in Agricultural Sciences Vol. 4

 Models are generated in this research to describe and forecast the effect of global cocoa prices on burnished color production in Ghana utilizing a regression model accompanying time series mistakes. The problem of price airiness is particularly definite for Ghana due to her heavy reliance on cocoa exports for convertibility earnings. The experiment sought to decide whether the all-encompassing cocoa price can aid in expecting the future behavior of Ghana's cocoa result. Annual data from 1961 to 2010 were used to fit the model, accompanying out-of-sample data from 2011 and 2012.  The reversion model with ARIMA(2,2,0) errors was considered the "best" model for the production changeable based on the results of abundant model adequacy techniques. The model was confirmed using the mean certain percentage mistake (MAPE) as forecast accuracy metric. Therefore, the 'best' reversion model's MAPE was 7.97%.  The usual "best" ARIMA model, still, which was fitted to the result variable, anticipated a MAPE of 16%. This demonstrates that when the result variable was modeled in addition to the world price utilizing regression accompanying ARIMA errors, the MAPE was reduced. Therefore, a reversion model with ARIMA (2,2,0) mistakes is a more accurate mathematical method than the ARIMA method for envisioning Ghana's cocoa result.

Author(s) Details:

Sampson Ankrah,
Postgraduate Institute of Agriculture, University of Peradeniya, Sri Lanka.

Kwadwo A. Nyantakyi,
Postgraduate Institute of Agriculture, University of Peradeniya, Sri Lanka.

E. Dadey,
SSNIT, Research Department, Accra, Ghana.

Please see the link here: https://stm.bookpi.org/EIAS-V4/article/view/10816

Study on Electricity Demand Forecasting Techniques and a Selection Strategy| Chapter 2 | Current Aspects in Business, Economics and Finance Vol. 6

 Based on thorough empirical studies, a study of plants of well-known electrical demand predicting approaches is presented in this place study. In addition, a selection planning for the approach of projecting power demand has been presented. The plan was grown with recommendation from experts in energetic demand forecasts and the World Bank's eight-factor model (through a questionnaire). On the support of time skyline, accuracy, complicatedness, skill level, dossier volumes, geographic inclusion, adaptability, and cost, the methods have been judged. The most widely used technique, in accordance with the experts, is ARIMA (Autoregressive Integrated Moving Average) accompanying exponential smoothing and Kalman cleaning. Artificial Neural Networks with preprocessed Linear and Fuzzy inputs are the second most favorite approach. Support Vector Regression, which is currently being checked by many electrical engineers complicated in electricity demand forecasts, concede possibility now replace this approach. In addition to these patterns that are highlighted, this study still provides ratings for alternative methods based on the World Bank's eight-determinant model.  This research gives taxonomy of important power demand forecasting methods that will be advantageous for practitioners, academics, and juniors. The report also contains expert opinions on the hierarchy of eight selection tests based on a technique choice policy paper from the World Bank.

Author(s) Details:

Paraschos Maniatis,
Department of Business Administration, School of Business, Athens University of Economics and Business, Patision 76, GR - 104 34, Athens, Greece.

Please see the link here: https://stm.bookpi.org/CABEF-V6/article/view/8736

Weighted Fuzzy Time Series Model to Forecast Epidemic Injuries and its Data Visualization | Chapter 5 | Current Overview on Science and Technology Research Vol. 5

 Globally, the coronavirus disease spread rapidly. To aid in decision-making, we must develop precise future projections of the number of COVID-19 infections. On the basis of accumulating global experience from which we are working to enhance our future methodologies to deal with such a pandemic and in an effort to find the best application, we present some of the techniques that have shown useful in predicting the number of people infected with COVID-19 in a specific period in different regions of the world in this paper.

We use a rewarding model to predict injuries in areas with COVID-19, particularly in the Arab region.

This prediction is based on Saudi Arabia's epidemic injury statistics from March 2 to July 20, 2020. Charts and graph visualisation are two alternative techniques to present time series data. We propose the use of weighted fuzzy time series methods (WFTS) and weighted non-stationary fuzzy time series techniques to compare with the conventional Auto-Regressive Integrated Moving Average (ARIMA) statistical method (WNSFTS). Before utilising the (ARIMA) and (WFTS) algorithms to forecast the given data, stationary data conversion is required because the data is not stationary. On our injuries dataset, we log transform and differentiate. The graphic depicts the situation for COVID in Saudi Arabia as improving. When we analyse the original data using the Dickey-Fuller Test (DFT), we find that the p-value is equal to 0.646, which is higher than 0.05 and shows non-stationarity. The mean square error (MSE), root mean square error (RMSE), and normalisation root mean square error (NRMSE) are used to compare the accuracy of the different techniques. The results show that WFTS approaches offer reliable assistance for calculating COVID-19-based pandemic injuries in the area. The use of Weighted Non Stationary Fuzzy Time Series (WNSFTS) can produce significantly better results when projecting the issue of epidemic injuries. because it can anticipate future infections and has a high level of predictive accuracy.

Author(s) Details:

Hala Ahmed Abdul-Moneim,
Department of Mathematics, Darb University College, Jazan University, Jazan, Saudi Arabia and Department of Mathematics, Faculty of Science, Minia University, Minia, Egypt.

Please see the link here: https://stm.bookpi.org/COSTR-V5/article/view/8335

Revised Study of Daily Time Series Data Analysis of Market Prices | Chapter 11 | Current Aspects in Business, Economics and Finance Vol. 2

 

This study aims to shed light on the occurrence of time series traits in the daily stock prices of securities traded on regulated exchanges. In contrast to earlier studies, this one focuses on real prices of traded assets as opposed to index numbers of daily stock market values. The idea of market efficiency and its application to the short-term forecasting of closing values of traded securities are further reasons why this work is significant. Additionally, the study contains evaluations of stock market responses to pandemic impacts (Scherf, Matschke, and Rieger, 2022) as well as approaches for forecast modification.

Author(s) Details:

Jeffrey Jarrett,
University of Rhode Island, US.

Please see the link here: https://stm.bookpi.org/CABEF-V2/article/view/7647

Statistical Modelling and Epidemiological Assessment of COVID-19 Fatality in India | Chapter 08 | Emerging Trends in Disease and Health Research Vol. 3

 With successive waves of illnesses, the world was badly devastated by the continuous spread of alpha, beta, and delta SARS-CoV-2 VoC. On November 25, 2021, a new SARS-CoV-2 variation of concern (VoC), omicron, was reported, almost 23 months after the first case of COVID-19 was recorded and after an estimated 260 million infections and 5.2 million fatalities globally. India has reported 32 instances of Omicron variant as of December 10, 2021. As a result, in order to manage and control the present pandemic, it is critical to identify and evaluate the risk. The study highlights India's shocking and unparalleled COVID-19 mortality. Case Fatality Rate (CFR) is the most adaptive epidemiological measure for evaluating illness severity, and it is calculated using the Yoshikura technique. When the estimated CFR of 10 Indian states is compared to the general CFR calculation, Kerala is determined to have the lowest CFR of 0.40 percent, signifying the least severe sickness. In order to obtain the best-fitted model with the data, Weibull, Gamma, and Lognormal probability distributions are used to model mortality in India. The Gamma distribution is the best-fitting probability model, according to the research. The trend and forecasting pattern of mortality are studied using time-series modelling. The ARIMA model predicts an upward trend in death in the following days, and this foresight model aids administrative authorities and medical staff in making health-care service and infrastructure plans. For the foreseeable future, public health initiatives including immunisation as a preventative method will be beneficial.

Author(S) Details

K. K. Jose
Department of Biostatistics, St. Thomas College, Palai, Kerala, India and School of Mathematics & Statistics, M. G. University, Kottayam, India

Liji Anna Varghese
Department of Biostatistics, St. Thomas College, Palai, Kerala, India and Sree Mookambika Institute of Medical Sciences, Kanyakumari, Tamil Nadu, India.

Vivek S. Nair
Department of Biostatistics, St. Thomas College, Palai, Kerala, India and Eurofins Advinus, Bangalore, India.

Jilby C. Jose
Department of Biostatistics, St. Thomas College, Palai, Kerala, India and  Inductive Quotient Analytics Pvt. Ltd., Hyderabad, India.

View Book:- https://stm.bookpi.org/ETDHR-V3/article/view/5879

Study on Time Series Modeling and Forecasting Inflation: Evidence from Nigeria | Chapter 10 | Insights into Economics and Management Vol. 4

Effective prediction of general price level trends has been a major concern of entrepreneurs and monetary authorities in Nigeria over the past decades. The results allow monetary authorities to prepare effectively and the profit drive on the part of entrepreneurs and investors continues. This research uses a univariate model developed by Box and Jenkins in the form of the Autoregressive Integrated Moving Average model and a multivariate time series model to forecast inflation for Nigeria in the form of the Vector Autoregressive model. Changes in the monthly use of this paper For the period 2003 to 2012, the consumer price index collected from the National Bureau of Statistics and the Central Bank of Nigeria forecast movements in the general price level. The best forecasting model for predicting inflation in Nigeria is defined on the basis of various diagnostic and evaluation parameters. The results would enable policymakers and businesses to monitor the output and stability of key macroeconomic indicators using the inflation forecast. For the month of 2012:10, the study expected core inflation using VAR to be 11.06 percent. A major drawback of this study is that the VAR method and the ARIMA method based on two major forecasting methods and ignored the use of neural network analysis. Furthermore, as an indicator of inflation, only core inflation was used. Subsequent inflation forecasting studies in Nigeria should aim to predict inflation through a broader variety of inflation steps.

Author (s) Details

Dr. Ikechukwu Kelikume
Lagos Business School, K.M 22 Lekki-Aja Expressway, Lagos Island, Lagos State, Nigeria.

Adedoyin Salami
Lagos Business School, K.M 22 Lekki-Aja Expressway, Lagos Island, Lagos State, Nigeria.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/348

Statistical Study on Food Grains Demand and Supply in India | Chapter 4 | New Perspectives in Agriculture and Crop Science Vol. 3

 In India, agricultural development policies have been aimed at reducing hunger, food insecurity, malnutrition and poverty at a rapid rate since independence in 1947. India accounts for 17% of the world's population, with a population of 1.11 million. In terms of customers, its population is always greater than the average size of other nations, with the exception of China. India's state of food shortage and malnutrition is of great importance to the global situation. In this present analysis, the predicted population and development of food grains are investigated using the ARIMA model and forecast decision-making. The projection of demand is also calculated using the near-ideal demand method (AIDS). This paper presents the status of demand-supply gaps. Total food grain production is projected to rise by 281.12 Mt in 2020, and the population will be 1345 million. Demand-supply scenarios currently present an alarming situation, especially in the case of pulses. The demand-supply difference for pulses is going to be more adverse, affecting our food and nutritional protection considerably. The demand for pulses will be 22.92 Mt in 2020, while the supply of pulses will be 19.68 Mt in that year. It is therefore reported that the demand-supply difference of pulses is negative. In order to boost technology, the improved process minimises the demand-supply gap in India for pulses.

Author(s) Details

Dr. Soumik Ray
Department of Agricultural Economics and Statistics, Centurion University of Technology and Management, MSSoA, Gajapati, Paralakhemundi- 761211, Odisha, India.

Dr. (Mrs.) Banjul Bhattacharyya
Department of Agricultural Statistics, Faculty of Agriculture, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia-741252,

West Bengal, India.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/293

Research on Time Series Analysis for Long Memory Process of Air Traffic Using ARFIMA | Chapter 1 | Recent Studies in Mathematics and Computer Science Vol. 3

 In the present study, the time series models ARIMA and ARFIMA or FARIMA models have been fitted to Air

India domestic air passengers, which considered as self similarity and Long Range Dependence (LRD). In such
case ARFIMA model is expected to be superior to ARIMA. We fitted ARIMA and ARFIMA models to air
traffic data and compared. Then the best model has been identified using RMSE, MAE and MAPE values. This
model can be useful to analyze the air traffic flow and revise the services of Air India. The analysis was carried
out using time series data on number of passengers travelling by Air India domestic flights during January 2012
to December 2018.

Author(s) Details

Manohar Dingari

Department of Mathematics, School of Technology, GITAM University, Hyderabad-502329, India.

D. Mallikarjuna Reddy
Department of Mathematics, School of Technology, GITAM University, Hyderabad-502329, India.

V. Sumalatha
Department of Statistics, OSMANIA University, Hyderabad, India.

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/233

Air Traffic Forecasting Using Time Series Models: New Perspectives | Chapter 4 | Emerging Trends in Engineering Research and Technology Vol. 4

In this chapter, Holt-Winters’ Additive model is fitted to the data regarding Domestic Air traffic in Air India flights. The investigation was done using dataset on number of passengers travelling by Air India domestic flights during January 2012 to November 2018. To prepare a tool to analyze the traffic flow monthly wise this helps Air India to revise their services. ARIMA model also has been fitted to the data, and compared with Holt-Winters’ Additive model. Finally, the results, findings and analysis proved that the Holt-Winters’ Additive model is superior to the ARIMA model for this data. This kind of analysis is very useful for forecasting the Air traffic.

Author(s) Details

Manohar Dingari
Department of Mathematics, GITAM University, Hyderabad-502329, India.

Dr. D. Mallikarjuna Reddy
Department of Mathematics, GITAM University, Hyderabad-502329, India.

V. Sumalatha
Department of Statistics, Osmania University, Hyderabad-500007, India.

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/180