Sibarama Panigrahi

National Institute of Technology Rourkela | NITR · Department of Computer Science and Engineering (CS)

Hierarchical forecasting (HF) methods are extensively utilized for precise decision-making by providing coherent forecasts across various levels. Traditionally, statistical models have been employed in HF. However, these static approaches often overlook the dynamic nature of the series during the aggregation and disaggregation of aperiodic and spon...

The endless adverse effects of air pollution incidents have raised significant public concerns in the past few decades. The measure of air pollution, that is, the air quality index (AQI), is highly volatile and associated with different kinds of uncertainties. Following this, the study and development of accurate fuzzy time series forecasting (TSF)...

Mathematical models derived from the physical systems are usually complex and in the form of higher order differential equations. Such systems are difficult for analysis and controller synthesis. Therefore, it is desirable to develop an efficient algorithm for reducing such higher order systems to a lower order model by preserving all the significa...

In this paper, individual and hybrid methods are proposed employing optimized statistical and deep learning (DL) models for deterministic (point) and probabilistic (interval) forecasting of crude oil price time series. The statistical models are optimized using the Forecast package of R. To enhance the performance of DL models, a novel pruning DE-D...

This paper presents a hybrid meta-heuristic algorithm using Grey Wolf optimization (GWO) and JAYA algorithm for data clustering. The idea is use exploitative capability of JAYA algorithm in the explorative phase of GWO to form compact clusters. Here, instead of using one best and one worst solution for generating offspring, three best wolfs and thr...

In this paper, a viable, robust, and highly accurate additive hybrid model employing autoregressive fractionally integrated moving average (ARFIMA) and support vector machine (SVM) with functionally expanded inputs (Additive-ARFIMA-SVM) is presented for forecasting the air quality index (AQI). Additionally, thirteen additive and multiplicative hybr...

In this paper, an extensive study is conducted to determine the most promising machine learning (ML) model among seventeen ML models including linear regression, lasso regression, ridge regression, elastic net, decision tree, random forest, K-nearest-neighbor regressor, Tweedie regressor, extra trees regressor, support vector regression, multilayer...

Over the last three decades, several researchers have been putting their efforts into developing non-deterministic fuzzy time series (FTS) models using the traditional fuzzy set. However, considering a set of membership values to each element of the time series, the hesitant fuzzy set glorifies the chances to capture the fuzziness and uncertainty d...

In a sensor cloud system, the sensors collect data and send it to the cloud system so that the end-users can access the data. When the frequency of data transmission is high, more power is consumed which reduces the finite battery lifetime of sensors. To reduce the data transmission between sensor and cloud without compromising the frequency of ava...

In this paper, we have introduced a differential perturbation operator into the gray wolf optimization (GWO) algorithm using three randomly selected omega wolves which assist the three leader wolves of the original GWO algorithm for diversifying the solution quality among the feasible omega wolves. Additionally, we have introduced the use of simila...

This paper presents a hybrid meta-heuristic algorithm using Grey Wolf optimization (GWO) and JAYA algorithm for data clustering. The idea is to use exploitative capability of JAYA algorithm in the explorative phase of GWO to form compact clusters. Here, instead of using one best and one worst solution for generating offspring, three best wolves and...

Proteins are linear polymers built from a repertoire of 20 different amino acids, which are considered building blocks of proteins. The diversity and versatility of these 20 building blocks with regard to their conformations are key to adopting three-dimensional structures that facilitate proteins to undergo important mechanistic biological process...

Grey wolf optimization (GWO) is one among the most promising swarm intelligence based nature inspired meta-heuristic algorithm that improves its search process by mimicking the search for prey and attacking strategy of grey wolfs. To further improve its performance, here we have hybridized with Jaya algorithm that improves the exploration capabilit...

In this paper, individual and hybrid methods are proposed employing optimized statistical and deep learning (DL) models for deterministic (point) and probabilistic (interval) forecasting of crude oil price (COP). The statistical models are optimized using Forecast package of R while a novel DE-DL method employing differential evolution (DE) algorit...

Over last few decades, partitional clustering algorithms have been emerged as one of the most promising clustering algorithms that find groups among data items. Motivated from this, we have proposed a hybrid sine-cosine algorithm (SCA) blended grey wolf optimisation (GWO) algorithm for partitional data clustering. This algorithm selects near-optima...

This study proposes a novel modified grey-wolf optimization algorithm (MGWOA) to enhance power system stability. The power system stabilizer and static synchronous series compensator (SSSC) are used as damping controllers. Additionally, fractional-order PID (FOPID) controller is used to handle the system nonlinearities and thus achieve better perfo...

In the modern digital era, with the availability of low-cost hardware like sensors and cameras, a huge amount of image databases are being created for diverse applications. These databases give rise to the need of developing efficient content-based image retrieval (CBIR) systems. Major efforts have been put over the past two decades to develop diff...

The conformational search is a highly complex optimization problem and the solution to such problem depends on the employed simulation techniques. The adaptation of novel techniques such as evolutionary algorithms could be more effective to solve the relevant problems specific to conformational search and even extended to global optimization.The ad...

In this article, a modified differential evolution (MDE) algorithm is proposed and applied to provide the solution for reactive power management by incorporating the flexible alternating current transmission systems (FACTS) controllers. The proper siting of FACTS controller has been achieved with an objective to minimize the losses and to improve t...

Accurate prediction of crop prices assists farmers to decide the best time to sell their produce so as to get maximum benefit and assists Government for post-harvest storage and management of the produce so as to stabilize the price volatility throughout the year. At the same time, pricing of crop depends on various factors including the amount of...

In this paper, a modified sine cosine algorithm (MSCA) is proposed and is applied to design the power system stabilizer (PSS) and lead lag compensator-based static synchronous series compensator (SSSC) controller to enhance the power system stability. Two test systems: single machine infinite bus (SMIB) and multi-machine power system (MMPS) working...

Over the years, numerous fuzzy time-series forecasting (FTSF) models have been developed to handle the uncertainty and non-determinism in the time-series (TS) data. To handle the non-determinism and indeterminacy, researchers have considered either intuitionistic fuzzy set or hesitant fuzzy set theory. However, in both the fuzzy set theories (FST),...

Universe of discourse (UOD) Number of interval (NOI) Length of interval (LOI) Ratio trend variation (RTV) Probabilistic intuitionistic fuzzy set (PIFS) Fuzzy logical relationships (FLRs) Support vector machine (SVM) A B S T R A C T The present research proposes a novel probabilistic intuitionistic fuzzy time series forecasting (PIFTSF) model using...

On-tree fruit monitoring is an important practice to provide the exact status of the fruits concerning its quality, quantity and degree of maturity in the farm. In large farm, it is difficult to look over the individual tree manually to acquire the knowledge about the fruits. Again, the manual inspection method is time-consuming, labor intensive an...

Solar activity directly influences the heliospheric environment and lives on the Earth. Sunspot number (SN) is one of the most crucial and commonly predicted solar activity indices. The prediction of SN time series is a challenging problem owing to its non-stationary, non-Gaussian and nonlinear nature. Therefore, improving the forecasting accuracy...

1 Over the past few decades, time series forecasting (TSF) has been predominantly performed using different artificial neural network (ANN) models. However, the performance of ANN models in TSF has not yet been fully explored due to several 1 issues like the determination of near-optimal ANN architecture for a time series and the efficiency of trai...

In the original publication, ‘ALGORITHM 1’ was missed out completely during typesetting.

In the past few years, non-stochastic fuzzy time series (FTS) models have drawn remarkable attention of researchers from different domains. Unlike traditional stochastic models, FTS models do not require any strict assumption on the characteristics of data to be modeled and are applicable to time series even with uncertainty. However, the effective...

Over the years Fuzzy Time Series (FTS) has used more popularly for forecasting the real time data. Generally in FTS method the membership value are not considering for the forecasting purpose, and it consider as a drawback in the forecasting process. Recently some researchers have overcome this problem by introducing the artificial neural network (...

Fuzzy time series forecasting (FTSF) methods avoid the basic assumptions of traditional time series forecasting (TSF) methods. The FTSF methods consist of four stages namely determination of effective length of interval, fuzzification of crisp time series data, modeling of fuzzy logical relationships (FLRs) and defuzzification. All the four stages...

Despite over more than twenty years of research on fuzzy time series forecasting (TSF) and several studies indicating superior performance, an appropriate computationally efficient method have not been developed to predict various time series using fuzzy TSF method. Motivated by this, in this paper a computationally efficient method is proposed to...

This paper presents a new hybrid ARIMA-ANN model for time series forecasting. In this model, the time series is first decomposed into low-volatile and high-volatile components using a fuzzy filter. The low-volatile component is modeled using ARIMA and high-volatile component is modeled using ANN. The final prediction is obtained by combining the pr...

Over the past few decades, a large literature has evolved to forecast time series using various linear, nonlinear and hybrid linear–nonlinear models. Recently, hybrid models by suitably combining linear models like autoregressive integrated moving average (ARIMA) with nonlinear models like artificial neural network (ANN) have become popular due to...

In this paper, an application of a hybrid Chemical Reaction Optimization (CRO) algorithm with adaptive Differential Evolution (DE) mutation strategies for training Higher Order Neural Networks (HONNs), especially the Pi-Sigma Network (PSN) is presented. Contrasting to traditional CRO algorithms, the reactant size (population size) remains fixed thr...

Over the past few decades clustering algorithms have been used in diversified fields of engineering and science. Out of various methods, K-Means algorithm is one of the most popular clustering algorithms. However, K-Means algorithm has a major drawback of trapping to local optima. Motivated by this, this paper attempts to hybridize Chemical Reactio...

time series forecasting (TSF) is of utmost importance in order to make better decision under uncertainty. Over the past few years a large literature has evolved to forecast time series using different artificial neural network (ANN) models because of its several distinguishing characteristics. This paper evaluates the effectiveness of three methods...

In this paper, an application of a novel chemical reaction optimization (CRO) algorithm for training higher order neural networks (HONNs), especially the Pi-Sigma Network (PSN) has been presented. In contrast to basic CRO algorithms, the proposed CRO algorithm used to train HONN possesses two modifications. The reactant size (population size) remai...

In this paper, an application of an adaptive differential evolution (DE) algorithm for training higher order neural networks (HONNs), especially the Pi-Sigma Network (PSN) has been introduced. The proposed algorithm is a variant of DE/rand/2/bin and possesses two modifications to avoid the shortcomings of DE/rand/2/bin. The base vector for perturba...