Journal of the Indian Society of Agricultural Statistics
The Society publishes an International Peer Reviewed Journal called Journal of the Indian Society of Agricultural Statistics with ISSN 0019-6363. Three issues of the Journal (April, August and December) are published annually. The first volume of the Journal was released in 1948. The Journal devoted to the publication of original research papers on all aspects of Statistics and Computer Applications preferably with innovative applications in Agricultural Sciences or that have a potential application in Agricultural Sciences. The review articles of the topics of current interest are welcome. The Journal also accepts books, monographs and periodicals for review. Special issues on thematic areas of both national and international importance are also brought out.
Indian Society of Agricultural Statistics (ISAS)
Millets in India: Evaluating the Current Scenario, Emerging Trends, Export Dynamics and Future Prospects
This study explores the evolving trends and future prospects of millets globally and within India, while also shedding light on the export dynamics of millets from India to importing countries. Globally, millet production has significantly declined from 1961 to 2022, with Asia remaining the primary contributor despite diminishing trends. In contrast, Africa has experienced growth in production due to expanded cultivation areas. In India, between 2000 and 2022, the cultivation area for small millets, jowar, bajra and ragi consistently declined, with compound annual growth rates (CAGR) of -5.60 per cent, -4.30 per cent, -1.70 per cent and -2.20 per cent, respectively. While the production of small millets and jowar experienced a decline, bajra production exhibited modest growth, increasing at an average rate of 1.6 per cent per annum. Nevertheless, productivity for these millets has improved, indicating better yields. Markov chain analysis highlights Bangladesh as the most reliable importer of Indian millet, maintaining a 96.85 per cent share, followed by the UAE at 51.29 per cent. Despite ongoing promotional efforts, significant challenges continue to hinder millet cultivation. This study underscores the need for strategic enhancements in export markets, productivity improvements and comprehensive outreach initiatives. By raising awareness and engaging consumers, the study aims to support the revival of millet cultivation and boost its trade in India.
Millets; Markov chain model; Millet trade; Growth rate etc.
Copyright (c) 2025 Journal of the Indian Society of Agricultural Statistics
Applications of Weibull Lomax Distribution: Review
In this paper, we reviewed the various works by different authors on Weibull Lomax distribution. The historical research based on some classical papers as well as ongoing research and advancements on Weibull Lomax distribution and its applications based on some latest papers were thoroughly reviewed and discussed in this paper. We mainly focused on various extensions of Weibull and Lomax models that have been used for developing a bathtub-shaped hazard function model for lifetime data. We also gave importance to study about the wide real applications of Weibull Lomax distribution in various fields including survival and lifetime data.
Bathtub hazard rate; Lifetime data; Lomax distribution; Monotone hazard rate; Weibull distribution; Weibull lomax distribution.
Construction of Almost Unbiased Estimator for Unknown Population Mean using Two Auxiliary Variables
In this paper, for estimating finite population mean of study character , an almost unbiased estimator, using two auxiliary variables is proposed. The usual ratio, product and ratio-cum-product estimators are biased. In some applications, biasedness of an estimator is disadvantageous. It is important to reduce it for better accuracy. Applying the procedure of Singh and Singh(1991,1993), in this paper, an almost unbiased estimator using two auxiliary variables is proposed for estimating finite population mean . The proposed estimator is almost unbiased up to first order of approximation. Expression for bias and mean square error (MSE) of the proposed estimator is derived up to the first order of approximation. To verify the theoretical findings an empirical study is carried out using two real data sets. One simulation study is also carried out which demonstrates that the bias of the proposed estimator is almost zero and the minimum MSE is equal to the MSE of the two variable regression estimator.
Unbiased estimator; Two auxiliary variables; Ratio-cum-product Estimator; Population mean estimation; Mean square error; Bias.
General Method of Construction of Constant Block Sum PBIB Designs With Two Replicates
A recent article by Yadav et al. (2024) presents two series of the constant block sum partially balanced incomplete block designs for number of treatments. The important feature was that the number of replicates r=2 and pairwise replication parameters ( λ, λ, λ)=(2,1,0). In the present article, it has been shown that with the same parameters values of r and λ, s, such restrictions can be relaxed and with the help of (nearly) magic rectangles, a much larger choice of designs can be made available.
Constant block-sum designs; Magic rectangles; Nearly magic rectangles; Partially balanced incomplete block designs.
A Comparative Analysis of Price Forecasting Models for Black Pepper
Black pepper being a perennial crop, the significant fluctuations in prices throughout the year pose a considerable challenge for both farmers and consumers. It is essential to understand the extent to which these price variations can be predicted in the near future to formulate pertinent policy recommendations. Consequently, the modelling and forecasting of time series data related to black pepper price hold paramount significance. Monthly price of garbled black pepper used for forecasting the price using Seasonal Autoregressive Moving Average model (SARIMA) and Time-delay Neural Network (TDNN) models. Comparison of models based on the accuracy measures, revealed TDNN as the best model for forecasting monthly price of black pepper.
Garbled black pepper; SARIMA; TDNN; Forecasting.
EEMD-FCR-TDNN: A Hybrid Model for Forecasting Agricultural Commodity Prices
This paper aims to develop a hybrid model using ensemble empirical mode decomposition (EEMD) as a decomposition technique and time-delay neural network (TDNN) as a forecasting technique to predict non-stationary and nonlinear agricultural price series. The EEMD first decomposes the agricultural price series into several intrinsic mode functions (IMFs) and a single residual. Further, the resulting IMFs and residual series are grouped into high frequency, low frequency, and a trend component with similar frequency characteristics to capture numerous coexisting hidden factors using the fine-to-coarse reconstruction (FCR) algorithm. After that, a TDNN with a single hidden layer is built to separately forecast each of the three nonlinear components. Finally, the prediction results of all three components are summed up to obtain a final output as the forecast of the original price series. The performance of the proposed hybrid EEMD-FCR-TDNN model is empirically evaluated by comparing it with several benchmark models, including the TDNN model and decomposition-ensemble hybrid models without reconstruction using monthly international maize and soybean oil price series. The results validate that the EEMD-FCR-TDNN model can significantly outperform the other models in terms of both level and directional prediction accuracy with lower computational cost.
Ensemble empirical mode decomposition; Fine-to-coarse reconstruction; Intrinsic mode function; Non-stationarity and non-linearity; Time-delay neural network.
A Short Review on Bayesian Estimation of a Common Coefficient of Variation from Inverse Gaussian Distributions
The coefficient of variation (CV) has been used in different disciplines with varied purpose related to variation in quantitative measurements. Statistical properties of CV have been studied by various researchers. Recently, a paper by the authors featuring an investigation of the posterior distribution of a common CV for inverse Gaussian populations with priors obtained through some empirical fitting procedure was presented by YPC at the 2022 ISBA World Meeting, June 26-July 1, 2022, held in Montreal, Canada. Some of these results along with other current developments by the authors on the topic were also reviewed during an invited presentation by MS in the honor of Dr. Daroga Singh at the 73rd Annual Conference of ISAS Conference held at the Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, November 14-16, 2022. The purpose of this paper is to summarize and discuss three key papers on estimation and testing of CV from inverse Gaussian distribution focussed on – a common CV (from multiple populations under frequentist framework), CV for a single population with Bayesian framework and a common CV from multiple populations and Bayesian framework, reviewed at this conference.
Common coefficient of variation; Inverse gaussian distribution; Bayesian inference; Conjugate priors; Lentil trials.
Wavelet Extreme Learning Machine (W-ELM) Model for Drought Index Forecasting
In an agriculturally depending country like India, accurate and reliable drought forecasting is very important to figure out how drought will affect water resources and agriculture. Data-driven machine learning forecasting techniques are promising approaches for drought forecasting since they take less development time, fewer inputs, and are less sophisticated than dynamic or physical models. Machine learning models for drought forecasting use drought indices that are more operational than raw climatic variables. In this study, the potential of wavelet-based extreme learning machine (W-ELM) model to forecast effective drought index has been explored for Sagar and Chhattarpur districts of the Bundelkhand region of India. The performance of W-ELM model has been compared with the other competitive machine learning models like support vector machine (SVM), extreme learning machine (ELM), and artificial neural network (ANN). Observational outcomes reveals that the W-ELM model outperforms ELM, SVM, and ANN.
Drought forecasting; ELM; SVM; ANN; Wavelet transformation.
Block Design for Two-Level Factorial Experiments in Block Size Four
In experimental scenarios characterized by one source of heterogeneity within the experimental material, block designs offer significant value. Exploring the optimal replication(s) required for factorial experiments, conducted in blocks of size four has garnered significant attention among researchers. While experiments in blocks of size two have been extensively studied, there is growing recognition that experiments in blocks of size four might offer greater utility in practical applications. Particularly, when estimating main effects and specific two-factor interactions from two-level factorial experiments conducted within blocks, a considerable number of replicates may be necessary. This article delves into the exploration of designs that minimize the required number of replications for factorial experiments conducted in blocks of size four. The article presents methodologies aimed at obtaining such designs, which hold promise for enhancing the efficiency and effectiveness of experimental investigations.
Block designs; n Factorial experiments; Confounding; Efficiency.
Prospects of Farmer Producer Organizations (FPOs) for Potato, Onion and Tomato: Indian Scenario
Vegetables are rich in vitamins, minerals, and antioxidants that are essential for human body. However, increase in urbanization coupled with improved purchasing power of consumers and enormous wastage during post-harvest handling and marketing created a huge gap between per capita demand and supply. This loss can be minimized by promoting Farmers’ Producer Organisation(FPO). In the present paper, status of FPOs engaged in perishable and important vegetable crops like tomato, potato and onion in different states of India have been studied. State-wise number of FPOs for tomato, potato and onion have been assessed and compared with production level of states using a composite index known as Vegetable Production Index. The Vegetable Production Index for each state has been constructed by combining production of potato, onion and tomato using principal component analysis and the states were categorized in high, medium and low categories using 75th percentile (0.40) and 25th percentile (0.01). The constructed index showed that Uttar Pradesh ranked first in production of these vegetables, followed by West Bengal and Maharashtra. It was investigated that except for few states, the states those were under high category, also had more number of FPOs (51%) engaged in potato, onion and tomato. The study showed that there is a need to increase number of FPOs for tomato, onion and potato in Chhattisgarh, Haryana and Odisha as they still have the opportunity to enhance marketing of vegetables through FPOs matching their production level.
Vegetable production index; Composite index; Farmer groups and institutionalization.
DDC: Deep Distribution Classifier, A Convolutional Neural Network-based Approach for Identifying Data Distributions
In domains such as the stock market and manufacturing, there’s a growing demand for faster and more accurate data distribution identification methods due to the rapid generation of vast volumes of data, highlighting the need for enhanced real-time decision-making capabilities. Traditional methods of identifying data distributions often rely on manual inspection, limited statistical tests and time-consuming analysis, leading to inefficiencies and inaccuracies in classification. In this scenario, the presented research offers a novel approach leveraging Deep Learning (DL) models to automate the process. The presented methodology also enables faster and more accurate identification of data distributions by the generation of synthetic data points and training of the DL model for identifying different distribution types. The primary objective of this study is to develop a DL model that categorizes data points into specific distributions based on an input dataset. Moreover, for model training and evaluation, a total of 1000 datasets are generated, each comprising 1000 data points. The study considers five distributions (Normal, Uniform, Exponential, Log-normal and Beta distribution), with 200 datasets generated (with randomly selected parameters) for each distribution. In the study, the DL model is trained first, and later, the model is evaluated on a separate test (unseen) dataset. Then, its performance in classifying the distributions is assessed based on metrics such as accuracy and loss. The study results demonstrate the effectiveness of the proposed approach in accurately classifying the distribution of data points, providing valuable insights into the application of DL for distribution classification tasks. The proposed method enhances scalability, robustness and efficiency by harnessing the power of convolutional neural networks and advanced preprocessing techniques.
Distribution identification; Deep learning; Data distribution classification; Convolutional neural networks; Fast distribution detection.
Identification of Paddy Stages from Images using Deep Learning
Rice, a crucial global staple, is integral to food security. Precise identification of paddy growth stages, booting, heading, anthesis, grain filling, and grain maturity is vital for agricultural decisions. However, a gap exists in recognizing these stages using red-green-blue (RGB) images. This study uses state-of-the-art computer vision and deep learning classification (Convolutional Neural Networks) algorithms to address this gap. Among the studied algorithms, EfficientNet_B0 achieved an impressive 82.8% overall accuracy. Notably, increasing image size from 64X64 pixels to 128X128 pixels significantly enhanced accuracy. A detailed assessment of growth stages revealed varying accuracy levels, with boot leaf being the most accurately detected (95.1%) and anthesis being the most challenging (72.28%). This work significantly advances automated monitoring, empowering researchers in real-time decision-making.
Paddy; Growth stages; Deep learning; Computer vision; Convolutional neural network.
Comparative Study of EMD based Modelling Techniques for Improved Agricultural Price Forecasting
Forecasting agricultural commodity prices is regarded as a challenging task due to its non-linear and non-stationary nature. As agriculture production is highly reliant on various biological and agro-meteorological factors, traditional smoothing techniques as well as statistical models often fail to model such series satisfactorily. To capture such complex patterns effectively, different data-driven and self-adaptive techniques have been developed time-to-time. Against this backdrop, in this paper, we have assessed the suitability of empirical mode decomposition (EMD)-based neural network and support vector regression (SVR) approaches for forecasting wholesale prices of three major potato markets namely, Agra, Bangalore, and Mumbai. As the benchmark models, autoregressive integrated moving average (ARIMA), time delay neural network (TDNN) and SVR models have been employed for the comparative evaluation. The experimental results clearly reveal the comparative superiority of the EMD-SVR model for the Agra and Bangalore markets and the EMD-TDNN model for the Mumbai market in terms of root mean squared error values and turning point predictions. Moreover, all the EMD-based models have performed better than the other competing models.
ARIMA; Empirical mode decomposition; SV; Price forecasting; Potato; TDNN.
On a Class of Bimodal Distributions and their Applications in Modelling Bimodal Error Data
A new family of bimodal distributions is introduced in this paper with an objective of using them for modelling error data sets. A new class of statistics arising from a symmetric distribution is proved to have distributions belonging to the family of the bimodal distributions introduced in this work. The information matrix is derived after addressing the problem of obtaining maximum-likelihood estimates for the parameters of generalized bimodal distribution. A simulation study is conducted to evaluate the properties of maximum likelihood estimators. The applications of the results in building bimodal distributions for some real life data sets are also illustrated.
Error data; Symmetric distributions; Bimodal distributions; Maximum likelihood estimate; Ordered density value induced statistics.
Selection of Best Subset of Weather Input through Step-wise Regression Method for Preharvest Cotton Yield Prediction in Western Agro-climate Zone of Haryana
Zonal-yield models incorporating a linear time trend and agro-meteorological (agromet) variables each spanning successive fortnights within the growth period of the cotton crop are developed within the framework of multiple linear regression analysis. These models have been used to predict the cotton yields in four cotton growing districts namely; Hisar, Bhiwani, Sirsa, Fatehabad covering more than 90% of cotton production of the Haryana State. Linear time-trend has been obtained using cotton yield data of the period 1980-81 to 2011-12. The fortnightly weather data along with trend yield have been utilized for the same period for building the zonal weather-yield models. Models have been validated for subsequent years i.e. 2012-13 to 2017-18, not included in the development of the models. The zonal models were fitted by taking DOA yield as dependent variable and fortnightly weather variables along with trend field/CCT/dummy variables as regressors. The predictive performance(s) of the contending models were observed in terms of average absolute percent deviations of cotton yield forecasts in relation to the observed yield(s) and root mean square error(s). The adequacy of the fitted models was examined through histogram, normal-probability plot for the residuals and residual plot against fitted values for the selected models. Although, the weather variables were found statistically significant as predictors and gave predictions with reasonably high coefficients of determination (R2) but the predictions had too high percent deviations to be acceptable and hence were deemed unsuitable for routine crop yield forecasting. To improve the predictive accuracy of the agromet yield models, a dummy regressor variable in the form of Crop Condition Term (CCT), was added to the weather models. The addition of CCT to the weather models significantly improved the accuracies of the district-level yield predictions in the State. The predictive performance of the zonal agromet models was assessed using multiple metrics, including the adjusted R2, the percent deviations of the forecast yields from the Department of Agriculture (DOA) yield estimates and the root mean square errors (RMSEs).
Keywords: Maximum temperature, Minimum temperature, Rainfall, Sun shine hours and relative humidity, Trend yield, Crop condition term.
A Stochastic Frontier Production Function to Measure the Technical Efficiency of the Farmer of Apple (Malus Pumila) Production and Productivity
This paper estimates the Technical Efficiency (T.E.) of an apple-producing farmer in Jubbal and Kotkhai tahsil of Shimla district, using a stochastic Cobb-Douglas (CD) production frontier function. A stochastic frontier production function can be used for panel data of firms. The non-negative technical inefficiency effects are assumed to function firm-specific variables and vary over time. They are believed to be independently distributed as truncations of normal distributions with constant variance. The result shows the mean technical efficiency of Jubbal and Kotkhai tahsil Farmer is 66% and 70%. The mean technical efficiency is high for Kotkhai Farmer compared to Jubbal Farmer. Therefore, it is concluded that Kotkhai’s farmers are more technically efficient than Jubbal with the same input. This suggests that Jubbal Farmer can potentially increase their productivity through more efficient use of information.
Technical efficiency, Stochastic frontier function (SFF), Constraints, Simple size.
Topic Modelling for Discovering Themes in the Queries Raised at Farmers’ Call Center
Topic modelling has gained prominence in the recent years due to the availability and necessities for the analysis of large volumes of unstructured text data. In agriculture, a huge amount of text data is generated in kisan call centers in the form of queries raised by the farmers. This study attempts to use the Latent Dirichlet Allocation method of topic modelling to discover the hidden topics in the queries raised at kisan call centers of five south Indian states. Through exploratory text analysis, it was found that the most common terms appeared in the query texts are ‘weather’, ‘management’ and ‘market’. The topic modelling lead to identification of 12 topics, out of which the topic ‘pest management in paddy, cotton and chilli’ reported the maximum number of queries.
Topic models; Latent Dirichlet Allocation; Text analysis; Kisan call center.
A Revisit to Satterthwaite Approximation to the Distribution of a Linear Combination of Sample Mean Squares
Linear combinations of mean squares occur in many areas of research like statistical hypothesis testing, design of experiments, and statistical genetics. The present paper demonstrates how the Satterthwaite approximation dating back to early nineteenth century is still a potential tool to reduce complicated problems to a solvable format. Significantly, a new derivation of the distribution of a linear combination of ‘Between sires’ and ‘Between dams within sires’ mean squares under certain restrictions is proposed which is quite simple compared the Graybill (1956) approach to the problem.
Linear combination of mean squares, Satherthwaite approximation.
Logarithmic type Direct and Synthetic Estimators using Bivariate Auxiliary Information with an Application to Real Data
This research work addresses bivariate auxiliary information-based logarithmic type direct and synthetic estimators for domain means in simple random sampling (SRS). The mean square error (MSE) of the suggested estimators is obtained, approximately to the first order. The efficiency standards by which the superiority of the suggested estimators is asserted are established. To demonstrate the superiority of the suggested estimators, a simulation investigation employing an artificially constructed normal population through the R programming language is also conducted. The analysis of real data from Swedish municipalities and the paddy crop acreage in the Mohanlal Ganj tehsil, Uttar Pradesh, India, also provides some applicability for the suggested estimators.
Domain means; Direct and synthetic estimators; Auxiliary information; Simple random sampling.
Generalized Dual to Ratio-cum-Product Type Estimators in Double Sampling for Stratificatio
Double sampling for stratification is a statistical technique used in survey sampling to improve the efficiency of estimating population parameters estimates. In the present paper we have proposed a generalized dual to ratio-cum-product type estimator of population mean. The expression for the MSE (Mean Square Error) of the proposed estimator has been derived upto first degree of approximation. The comparison of the proposed estimator with Ige and Tripathi (1987) and Lone et al. (2020) estimators in double sampling for stratification indicated that proposed estimator is more efficient than the other estimators given in the literature. Motivated by Tailor and Lone (2014), the generalised version of the proposed estimator is also suggested in the present paper. The empirical study indicated that the proposed estimator is more efficient than the other estimators
Double sampling for stratification, Mean square error.
Upper Limit of Coefficient of Variation for Improving Efficiency in Sugarcane Field Experiments in India
Coefficient of variation (CV) is used to measure the heterogeneity of the experimental field in the context of agricultural research including sugarcane experiments. Smaller the CV value of cane yield, higher is the reliability of the experiment. In all the zones of All India Coordinated Research Project on Sugarcane - AICRP(S), upper limit of CV for cane yield is higher for early varietal trials in comparison to the mid to late varietal trials of sugarcane in India. Similar trend was also found for upper limit of CV for sucrose (%). Upper limit of CV (%) for cane yield was found highest in Peninsular zone as around 10% in early varietal trial followed by North central Zone. It is also observed as lowest in North West Zone, which is around 8.5 % both for early and mid-late varietal trials. Same trend was also observed for sucrose, which around 3% at 1%, 5% and 10% level of significance. Overall for all the trial of cane yield, it is estimated as 9% for cane yield in the country at all the three level of significance. For plant crop, it is estimated as 9% at all level of significance. For ratoon crop, it is estimated as 10% at all level of significance.
Coefficient of variation; Randomized block design; Upper limit; AICRP(S).
Generalized Class of Some Novel Estimators under Ranked Set Sampling
In this paper, mean estimators under ranked set sampling are reviewed. In this paper, we have also presented some improved novel classes of estimators for estimating the population mean using auxiliary variable under ranked set sampling. We have derived the expressions for bias and mean squared error of the proposed estimators up to the first order of approximation and the proposed classes of estimators are found to be more efficient than the other estimators in this study. In an attempt to verify the efficiencies of proposed estimators, theoretical results are supported by empirical study.
: Study variable; Auxiliary variable; Bias, Mean square error; Ranked set sampling
Sampling Methodology for Estimation of Private Food grains Stock at Farm Level Aligned with Input Survey of Agriculture Census in India
In India, Food and Agriculture Organization of the United Nations (FAO) was implementing a project “Strengthening Agriculture Market Information System (AMIS) in India using Innovative Methods and Digital Technology” and supporting the efforts of the Ministry of Agriculture and Farmers Welfare, Govt. of India. This project identified the potential of improving the data coverage on ‘on-farm’ post-harvest management of food grains through Input Survey carried out in Agriculture Census. Therefore, a pilot study on private food grains stock estimation at farm level aligned with Input Survey of Agriculture Census in India funded by FAO-India was conducted by ICAR-Indian Agricultural Statistics Research Institute (ICAR-IASRI). Under this study, a suitable sampling methodology aligned with existing Input Survey for estimation of private food grain stock at farm level has been developed. A suitable questionnaire aligned with existing Input Survey of Agriculture Census has been developed covering different food grains stock at farm level. Under this study, a pilot survey was conducted in two states namely Haryana and Madhya Pradesh. The four crops under AMIS study i.e. wheat, paddy, maize and soybean along with pulses were covered under this pilot survey. The data was collected for all the three seasons. The estimates of food grains stock, pre-harvest opening stock, production obtained, quantity sold, quantity stored, quantity disposed and percentage stock at farm level were obtained along with its percentage Coefficient of Variation (% CV) and were found to be reasonably good for overall size classes. Therefore, it is expected that for overall holding size classes, the proposed methodology will provide farm level reliable estimates of food grains stock at district level. The study has established the feasibility of inclusion of developed questionnaire in the future Input Survey of Agriculture Census in India in order to estimate the food grains stock at farm level which will bridge the gap on private food grains stock in on-farm and off-farm domains of the supply chain.
Food grains stock; Input survey; Agriculture Census; Supply chain; Percentage stock, Sampling methodology.
Favourable Allocation Models for Symmetric Distributions in Ranked Set Sampling
Kaur et al. (2000) suggested an optimal allocation model for symmetric distribution in ranked set sampling (RSS). This model is not much applicable due to its dependency upon extreme or mid order statistic only. In this paper, an attempt to make a “favourable” and near optimal allocation has been made by allocating each rank order at least once and considering the opposite behavior of Neyman allocation for symmetric distribution. The case of perfect ranking is considered. The utility of the proposed model in terms of relative precision has been shown for some symmetric distributions. Favourable model outperforms both for equal and Neyman’s allocations and quite close to the optimal model for each set size. The model will provide a practical approach in situations where RSS is likely to lead to an improvement over simple random sampling and the underlying distribution is symmetric.
KPT model; Neyman’s allocation; Order statistic; Ranked set sample; Relative precision; Symmetric distribution. 2020 AMS Mathematics Subject Classification: 62D05; 62G09; 94A20
LSTM based Stacked Autoencoder Approach for Time Series Forecasting
This study proposes a novel approach for multi-step time series forecasting using a stacked long-short term memory (LSTM) sequence-to-sequence autoencoder (LSTM-SAE) to handle the volatility of edible oil prices in the Indian market. The approach was implemented on Ruchi Soya Ltd. stock price dataset and compared with other deep learning models like Gated Recurrent Unit (GRU), LSTM, and Bi-directional LSTM. The LSTM SAE outperformed other models in closing price prediction based on evaluation metrics like Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The proposed approach has significant implications for stakeholders in the edible oil and oilseeds industry, including farmers, traders, and policymakers
Deep learning, LSTM, Autoencoder, Gated Recurrent Unit (GRU), Stock price.
Applications of Remote Sensing and GIS in Crop Surveys: An IASRI Perspective
I consider it a great honor to be called upon to prepare a paper on birth centenary of Dr. Daroga Singh, Former Director of the Institute. I am grateful to ISAS and especially Dr. Padam Singh, Executive President, ISAS for giving me this opportunity. I am greatly indebted to Dr. Daroga Singh for his generosity, kindness, guidance and support right from my student days at IASRI and his inspiration and guidance as Chairman of my Advisory Committee for both my M.Sc. Degree and Ph.D Degree at IASRI and in Delhi University. After I joined service at IASRI, Dr. Daroga Singh remained a great inspiration and support for which I will remain indebted to him throughout my life. During 1980’s, NATP Programme sponsored by UNDP was implemented at ICAR and its research institutes to give a thrust to application of the new and advanced technologies in different fields of Agricultural research to boost the production and research. During this period, Dr. Prem Narain has been the Director of the Institute. He had a great vision and zest to work and during his time, the research activities of the Institute expanded vastly. He had great belief in the enormous strength of the Remote Sensing technology and hence it was recognized as a new thrust area of research under NATP and I was given the responsibility to initiate research programs in this field. As Dr. D. Singh had been great supporter of new technology therefore, we have therefore decided to present work done at IASRI in the area of Remote Sensing and Geo-Informatics in this paper, which is dedicated to him.
Remote sensing; Geographical information system; Spatial sampling; Spatial modeling.
Linear Integer Programming and its Innovative Applications in Design of Experiments and Sample Surveys
Linear integer programming is a widely used optimization technique to solve various real life problems. The purpose of this article is to present some innovative applications of linear integer programming in the area of design of experiments and sample surveys. It is demonstrated how construction problems of various block designs and different classes of sampling plans can be solved using linear integer programming formulations.
Linear integer programming; Block designs; Balanced; Controlled Sampling; Distance balance; Inclusion probability.
Network Sampling for Estimation of the Size of a Finite Population with Special Features
We are interested in unbiased estimation of the unknown size of a finite population. The population units [also called Ultimate Units (UUs)] are not directly accessible in any way. We can only have access to any UU via an appropriate Reference Unit [RU] only if it captures the UU in question. The literature is scanty and the state of knowledge also seems to be imperfect. We aim at providing an overview of the literature in this fascinating area of research and its application.
Estimation of size of specially featured population; Reference units; Ultimate units; Probing and no-probing; Bipartite Di-Graph; Networking.
Fixing Size of a Varying Probability Sample in a Direct and a Randomized Response Survey
For a Direct Survey on innocuous characteristics Chebyshev’s inequality is helpful in prescribing the size of a sample in a survey. An extension of the same to cover stigmatizing features in Randomized Response (RR) survey is not smooth enough. Different situations are illustrated and solutions proposed. AMS subject classification: 62D05.
Randomized response survey; Sample-size fixation; Varying probability sampling.
Robust Estimation in Finite Population Sampling under Model based Prediction Approach
In the present paper, some important contributions on estimation of finite population parameters under model-based prediction approach are reviewed. Following Scott et al. (1978), a BLU predictor of population total in model-based prediction approach under the model 2 (0,1: ) hk ξ x in stratified sampling when the slope of the model is common across the strata is constructed. Its robustness and optimality is studied when some general polynomial model of degree j, i.e., 2 0 1 ( , ,....., : ) J hk ξδ δ δ x is true in real practice. It has been found that the proposed predictor is robust and optimal for stratified balanced sample and it is also more efficient than the predictor developed by Scott et al. (1978) when slopes are uncommon across the stratum.
Super population models; Stratified sampling; Balanced sample; Overbalanced sample; General polynomial models.