An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 5 | Issue - 3 | september 2023
Published
30 October, 2023
This research article proposes an innovative strategy to improve long-term forecasting accuracy for gasoline sales in Canada. The SARIMA-GARCH model was used with the rolling window forecasting technique to successfully address varying seasons, changing patterns, and conditional variance on the historical data of gasoline sales in Canada (1993-01-01 to 2015-12-01) with the sample size of 276. The rolling window forecasting technique was used to forecast one-step-ahead value and update the model to fresh observations while minimizing look-back bias and attaining good long-term forecasting accuracy. The findings revealed considerable improvements in forecasting accuracy. The proposed SARIMA-GARCH model with rolling window forecasting produced a RMSE of 151026.28 and a Mean Absolute Percentage Error (MAPE) of 0.0340. This outperformed other baseline models, including simple SARIMA model which had a RMSE of 329,689.88 and a MAPE of 0.0786, and the GARCH model which had a RMSE of 316,168.33 and a MAPE of 0.0685. The data shows that the proposed approach is effective for accurate long-term forecasting of gasoline sales in Canada. The study provides significant data for politicians, industry professionals, and energy investors, assisting them in making informed decisions about resource allocation, strategic planning, and risk management.
KeywordsTime Series Forecasting SARIMA Gasoline Prediction GARCH ARCH Hybrid Forecasting Model SARIMA-GARCH Expanding Rolling Window Forecasting
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