Forecasting Retail Sales for Furniture and Furnishing Items through the Employment of Multiple Linear Regression and Holt–Winters Models

Systems, Journal Year: 2024, Volume and Issue: 12(6), P. 219 - 219

Published: June 19, 2024

Global economic growth, marked by rising GDP and population, has spurred demand for essential goods including furniture. This study presents a comprehensive forecasting analysis retail furniture sales in the U.S. next 36 months using Multiple Linear Regression (MLR) Holt–Winters methods. Leveraging data from 2019 to 2023, alongside key influencing factors such as imports, consumer sentiment, housing starts, we developed two predictive models. The results indicated that exhibited strong seasonality positive trend, with lowest forecasted April 2024 (USD 9118 million) highest December 2026 13,577 million). average annual 2024, 2025, is projected at USD 12,122.5 million, 12,522.67 12,922.17 respectively, based on MLR, while are slightly more conservative. models were compared Mean Absolute Percentage Error (MAPE) metric, MLR model yielding MAPE of 3.47% achieving 4.21%. study’s findings align global market projections highlight growing trajectory industry, providing valuable insights strategic decision-making operations management.

Language: Английский

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A review on digital twin application in photovoltaic energy systems: challenges and opportunities

JMST Advances, Journal Year: 2024, Volume and Issue: 6(3), P. 257 - 282

Published: July 20, 2024

Language: Английский

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Wind power plant site selection problem solution using GIS and resource assessment and analysis of wind energy potential by estimating Weibull distribution function for sustainable energy production: The case of Bitlis/Turkey

Energy Strategy Reviews, Journal Year: 2024, Volume and Issue: 56, P. 101552 - 101552

Published: Oct. 15, 2024

Language: Английский

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Recent increase in soil moisture levels concerning climate variability in the karst region of southwest China using wavelet coherence and multi-linear regression

Gondwana Research, Journal Year: 2025, Volume and Issue: 141, P. 40 - 54

Published: Feb. 7, 2025

Language: Английский

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Machine learning-enhanced analysis of liquid nitrogen spreading on superheated walls: An artificial neural network-based investigation

Thermal Science and Engineering Progress, Journal Year: 2025, Volume and Issue: unknown, P. 103392 - 103392

Published: Feb. 1, 2025

Language: Английский

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Effect of abrasive water-jet treatment on surface characteristics and epoxy adhesive bonding performance of carbon steel

Progress in Organic Coatings, Journal Year: 2025, Volume and Issue: 201, P. 109137 - 109137

Published: Feb. 15, 2025

Language: Английский

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PV Solar Power Forecasting Using a Subset of Characteristic Features Selected by AI Methods

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 186 - 195

Published: Jan. 1, 2025

Language: Английский

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The effect of using automatic meter reading system in electricity distribution systems on reducing non-technical losses: The case of Bitlis province in Turkey

Energy Strategy Reviews, Journal Year: 2025, Volume and Issue: 58, P. 101674 - 101674

Published: March 1, 2025

Language: Английский

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Development of a machine-learning-based performance prediction model for indirect regenerative evaporative cooling applications supported by experimental and numerical techniques

Journal of Thermal Analysis and Calorimetry, Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Language: Английский

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Design and implementation of outdoor home smart farming based on raspberry pi for home assistant management

Multidisciplinary Science Journal, Journal Year: 2025, Volume and Issue: 7(8), P. 2025418 - 2025418

Published: Feb. 8, 2025

Outdoor home smart farming, also known as urban is the practice of cultivating fruits, herbs, or vegetables for personal consumption on a small scale within residential area. It more sustainable compared to conventional agriculture in all aspects. However, there were various challenges implementing outdoor including limitation lack skills, resources, infrastructure produce good and high-quality crops. This study addresses these by integrating Python scripting Linux OS with hardware components like Raspberry Pi 4 Model B, ESP32, soil moisture sensors, UV lights. Home Assistant, an open-source software, was utilized run script programming farming. The integrated devices into Assistant used monitor analyze farming parameters outcomes assist decision-making provide further user action. As result, system efficient due only consuming 0.16% water usage 0.64% energy daily household use, well reducing up 82.45% watering process. These results highlight system’s capability optimize resource enhance crop productivity while minimizing environmental impact. By leveraging IoT frameworks, showcases how modern technology can revolutionize traditional practices. automated not reduces manual labor but provides real-time data actions. In conclusion, implementation management based effectively agriculture.

Language: Английский

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