Price Prediction of Seasonal Items Using Machine Learning and Statistical Methods Article Swipe
Mohamed Ali Mohamed
,
Ibrahim Elhenawy
,
Ahmad Salah
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2022.020782
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2022.020782
Price prediction of goods is a vital point of research due to how common e-commerce platforms are. There are several efforts conducted to forecast the price of items using classic machine learning algorithms and statistical models. These models can predict prices of various financial instruments, e.g., gold, oil, cryptocurrencies, stocks, and second-hand items. Despite these efforts, the literature has no model for predicting the prices of seasonal goods (e.g., Christmas gifts). In this context, we framed the task of seasonal goods price prediction as a regression problem. First, we utilized a real online trailer dataset of Christmas gifts and then we proposed several machine learning-based models and one statistical-based model to predict the prices of these seasonal products. Second, we utilized a real-life dataset of Christmas gifts for the prediction task. Then, we proposed support vector regressor (SVR), linear regression, random forest, and ridge models as machine learning models for price prediction. Next, we proposed an autoregressive-integrated-moving-average (ARIMA) model for the same purpose as a statistical-based model. Finally, we evaluated the performance of the proposed models; the comparison shows that the best performing model was the random forest model, followed by the ARIMA model.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/cmc.2022.020782
- https://www.techscience.com/cmc/v70n2/44726/pdf
- OA Status
- diamond
- Cited By
- 20
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3203480922
All OpenAlex metadata
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https://openalex.org/W3203480922Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32604/cmc.2022.020782Digital Object Identifier
- Title
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Price Prediction of Seasonal Items Using Machine Learning and Statistical MethodsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-09-27Full publication date if available
- Authors
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Mohamed Ali Mohamed, Ibrahim Elhenawy, Ahmad SalahList of authors in order
- Landing page
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https://doi.org/10.32604/cmc.2022.020782Publisher landing page
- PDF URL
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https://www.techscience.com/cmc/v70n2/44726/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://www.techscience.com/cmc/v70n2/44726/pdfDirect OA link when available
- Concepts
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Autoregressive integrated moving average, Random forest, Computer science, Support vector machine, Machine learning, Context (archaeology), Statistical model, Artificial intelligence, Autoregressive model, Econometrics, Time series, Economics, Geography, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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20Total citation count in OpenAlex
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2025: 4, 2024: 6, 2023: 7, 2022: 3Per-year citation counts (last 5 years)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.learning | 31, 147 |
| abstract_inverted_index.problem. | 86 |
| abstract_inverted_index.proposed | 101, 133, 154, 174 |
| abstract_inverted_index.research | 9 |
| abstract_inverted_index.seasonal | 66, 79, 116 |
| abstract_inverted_index.utilized | 89, 120 |
| abstract_inverted_index.Christmas | 69, 96, 125 |
| abstract_inverted_index.conducted | 21 |
| abstract_inverted_index.evaluated | 169 |
| abstract_inverted_index.financial | 43 |
| abstract_inverted_index.platforms | 15 |
| abstract_inverted_index.products. | 117 |
| abstract_inverted_index.real-life | 122 |
| abstract_inverted_index.regressor | 136 |
| abstract_inverted_index.algorithms | 32 |
| abstract_inverted_index.comparison | 177 |
| abstract_inverted_index.e-commerce | 14 |
| abstract_inverted_index.literature | 57 |
| abstract_inverted_index.performing | 182 |
| abstract_inverted_index.predicting | 62 |
| abstract_inverted_index.prediction | 1, 82, 129 |
| abstract_inverted_index.regression | 85 |
| abstract_inverted_index.performance | 171 |
| abstract_inverted_index.prediction. | 151 |
| abstract_inverted_index.regression, | 139 |
| abstract_inverted_index.second-hand | 51 |
| abstract_inverted_index.statistical | 34 |
| abstract_inverted_index.instruments, | 44 |
| abstract_inverted_index.learning-based | 104 |
| abstract_inverted_index.cryptocurrencies, | 48 |
| abstract_inverted_index.statistical-based | 108, 165 |
| abstract_inverted_index.autoregressive-integrated-moving-average | 156 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.90896317 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |