SOURAV KUMAR PUROHIT

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Engineering, Computer Science, Computer Science Applications

Scopus Publications

Accurate Air Quality Index Prediction Using Variational Mode Decomposition and Stacked Ensemble Learning
Sushree Subhaprada Pradhan, Kedar Kumar Dora, Sourav Kumar Purohit
Communications in Computer and Information Science, 2026
Decomposition-based hybrid methods employing statistical, machine learning, and deep learning models for crude oil price forecasting
Sourav Kumar Purohit, Sibarama Panigrahi
Neural Computing and Applications, 2025
Crude Oil Price Forecasting Using Hybridization of Optimized Deep Learning and Shallow Machine Learning Models
Sourav Kumar Purohit, Sibarama Panigrahi, Aditya Narayan Jena
Communications in Computer and Information Science, 2025
Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models
Sourav Kumar Purohit, Sibarama Panigrahi
Information Sciences, 2024
Forecasting Crude Oil Prices: A Machine Learning Perspective
Sourav Kumar Purohit, Sibarama Panigrahi
Communications in Computer and Information Science, 2024
Study and development of hybrid and ensemble forecasting models for air quality index forecasting
Sushree Subhaprada Pradhan, Sibarama Panigrahi, Sourav Kumar Purohit, Jatindra Kumar Dash
Expert Systems, 2023
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 hybrid models are introduced. Several alternatives in feature engineering employing functional expansion of inputs are incorporated to boost the performance of hybrid models. Furthermore, a gradient whale optimization algorithm with group best leader strategy (GWOA‐GBL) based meta‐heuristic algorithm is proposed. The missing values are imputed and a variable weight ensemble forecasting model is developed using the proposed GWOA‐GBL algorithm. To evaluate the effectiveness of the proposed Additive‐ARFIMA‐SVM forecasting model with functionally expanded inputs, comparisons are made with sixteen machine learning models, including long short‐term memory (LSTM), five statistical models, seventeen hybrid models, and ten variable weight ensemble models. Extensive statistical analyses are carried out on the obtained results considering four accuracy measures that show the statistical supremacy of the proposed Additive‐ARFIMA‐SVM model and GWOA‐GBL algorithm in predicting the AQI time series. The proposed Additive‐ARFIMA‐SVM model with functionally expanded inputs improves the AQI forecasting performance by 16.34% than autoregressive integrated moving average, 14.47% than ARFIMA, 33.96% than XGBoost, 43.47% than SVM, 49.39% than LSTM, 8.64% than Multiplicative‐ARIMA‐SVM model considering symmetric mean absolute percentage error. The proposed Additive‐ARFIMA‐SVM model is so efficient and reliable that it can be applied to forecast other time series like stock price, electricity load, crude oil price, sunspot number, stream flow, flood, drought etc.
Time Series Forecasting of Price of Agricultural Products Using Hybrid Methods
Sourav Kumar Purohit, Sibarama Panigrahi, Prabira Kumar Sethy, Santi Kumari Behera
Applied Artificial Intelligence, 2021

RECENT SCHOLAR PUBLICATIONS

Accurate Air Quality Index Prediction Using Variational Mode Decomposition and Stacked Ensemble Learning
SS Pradhan, KK Dora, SK Purohit
International Conference on Computing, Communication and Learning, 207-222 , 2025
2025.0
Decomposition-based hybrid methods employing statistical, machine learning, and deep learning models for crude oil price forecasting
SK Purohit, S Panigrahi
Neural Computing and Applications 37 (18), 12565-12610 , 2025
2025.0
Citations: 4
Ranking Optimised Statistical Models for Time Series Forecasting of Crude Oil Price
SK Purohit, S Panigrahi
Computing, Communication and Intelligence, 177-181 , 2025
2025.0
Citations: 1
Crude oil price forecasting using hybridization of optimized deep learning and shallow machine learning models
SK Purohit, S Panigrahi, AN Jena
International Conference on Computing, Communication and Learning, 3-16 , 2024
2024.0
Citations: 2
Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models
SK Purohit, S Panigrahi
Information Sciences 658, 120021 , 2024
2024.0
Citations: 37
Study and development of hybrid and ensemble forecasting models for air quality index forecasting
SS Pradhan, S Panigrahi, SK Purohit, JK Dash
Expert Systems 40 (10), e13449 , 2023
2023.0
Citations: 8
Forecasting crude oil prices: a machine learning perspective
SK Purohit, S Panigrahi
International Conference on Computing, Communication and Learning, 15-26 , 2023
2023.0
Citations: 5
Time series forecasting of price of agricultural products using hybrid methods
SK Purohit, S Panigrahi, PK Sethy, SK Behera
Applied Artificial Intelligence 35 (15), 1388-1406 , 2021
2021.0
Citations: 113
A Novel Series-Parallel Hybrid Method Employing Fusion of Deep Features for Air Quality Index Forecasting
SS Pradhan, S Panigrahi, SK Purohit
Available at SSRN 5275908 , 0

MOST CITED SCHOLAR PUBLICATIONS

Time series forecasting of price of agricultural products using hybrid methods
SK Purohit, S Panigrahi, PK Sethy, SK Behera
Applied Artificial Intelligence 35 (15), 1388-1406 , 2021
2021.0
Citations: 113
Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models
SK Purohit, S Panigrahi
Information Sciences 658, 120021 , 2024
2024.0
Citations: 37
Study and development of hybrid and ensemble forecasting models for air quality index forecasting
SS Pradhan, S Panigrahi, SK Purohit, JK Dash
Expert Systems 40 (10), e13449 , 2023
2023.0
Citations: 8
Forecasting crude oil prices: a machine learning perspective
SK Purohit, S Panigrahi
International Conference on Computing, Communication and Learning, 15-26 , 2023
2023.0
Citations: 5
Decomposition-based hybrid methods employing statistical, machine learning, and deep learning models for crude oil price forecasting
SK Purohit, S Panigrahi
Neural Computing and Applications 37 (18), 12565-12610 , 2025
2025.0
Citations: 4
Crude oil price forecasting using hybridization of optimized deep learning and shallow machine learning models
SK Purohit, S Panigrahi, AN Jena
International Conference on Computing, Communication and Learning, 3-16 , 2024
2024.0
Citations: 2
Ranking Optimised Statistical Models for Time Series Forecasting of Crude Oil Price
SK Purohit, S Panigrahi
Computing, Communication and Intelligence, 177-181 , 2025
2025.0
Citations: 1
Accurate Air Quality Index Prediction Using Variational Mode Decomposition and Stacked Ensemble Learning
SS Pradhan, KK Dora, SK Purohit
International Conference on Computing, Communication and Learning, 207-222 , 2025
2025.0
A Novel Series-Parallel Hybrid Method Employing Fusion of Deep Features for Air Quality Index Forecasting
SS Pradhan, S Panigrahi, SK Purohit
Available at SSRN 5275908 , 0