Demand Forecasting For Furniture Industry With Multi-Variable Time Series Models

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This is a Observational study (also classified as: Literature Review).

Burak Güngör; Emine Tokgöz; Ali Sevinç; Eren Kamber; Mehmet Gümüş

doi:10.46740/alku.1542106

August 26, 2025ALKÜ Fen Bilimleri DergisiOpen Access

Demand Forecasting For Furniture Industry With Multi-Variable Time Series Models

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Key Points

ARIMA-SARIMAX outperforms Holt-Winters and Multiple Linear Regression, enhancing forecast accuracy for furniture sales.
The study utilized real-life sales data from 2005 to 2019 to analyze demand patterns and influence factors.
Incorporating variables like inflation and interest rates provides insights for better demand predictions and planning.
Advanced forecasting methods help businesses maintain competitive advantage by optimizing inventory levels and strategies.

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Implication

Analysis reveals superior accuracy in demand forecasting for the furniture industry using time series models, indicating ARIMA-SARIMAX outperforms others.

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Cite This Study

Güngör et al. (2025) studied this question.

synapsesocial.com/papers/68af6203ad7bf08b1eae2cbe https://doi.org/10.46740/alku.1542106

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Demand Forecasting For Furniture Industry With Multi-Variable Time Series Models

Key Points

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Implication

Discussion

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Demand Forecasting For Furniture Industry With Multi-Variable Time Series Models

Key Points

No abstract yet

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Implication

Discussion

Cite This Study

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