Abstract
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.
Highlights
Today, making a decision about buying or selling any assets or gifts is a very difficult process.A lot of factors and complexities can influence your decision, such as the best time for buying or selling the products, goods, or seasonal gifts
There is a vital need for models that can predict the prices of seasonal goods and the ability to compare the performance of the machine learning model against the statistical models
The background discusses the theory behind the following algorithms: Linear regression, ridge regression, support vector regression (SVR), random forest regression (RF), and ARIMA algorithms
Summary
Today, making a decision about buying or selling any assets or gifts is a very difficult process. Examples include price forecasting for coal, electricity, natural gas, houses, cars, prediction of Bitcoin prices, and second-hand ecommerce price projection Despite these efforts, there is no existing research providing a model for predicting the prices of seasonal goods, to our best knowledge. There is a vital need for models that can predict the prices of seasonal goods and the ability to compare the performance of the machine learning model against the statistical models. These proposed models can help the seller evaluate the proper seasonal goods pricing, which attracts clients and increases profits based on historical data.
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