A Novel Hybrid Deep Learning Model For Stock Price Forecasting

Alghamdi, Dhaifallah; Alotaibi, Faris; Rajgopal, Jayant

doi:10.1109/ijcnn52387.2021.9533553

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…The results of the numerical tests showed that the deep neural DNN-based models performed better than the rest of the models for predicting price swings up and down, while the LSTM models performed better than the rest of the models for predicting the price of Bitcoin. DL was utilized by the authors of [20] to investigate the problem of generating reliable multi-step ahead stock price predictions for the selected company. They proposed a feature-learning system with a new model architecture for multi-step-ahead stock price forecasting.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Price Prediction of Seasonal Items Using Time Series Analysis

Salah¹,

Bekhit²,

Eldesouky³

et al. 2023

Computer Systems Science and Engineering

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The price prediction task is a well-studied problem due to its impact on the business domain. There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change, but there is very limited work to study the price prediction of seasonal goods (e.g., Christmas gifts). Seasonal items' prices have different patterns than normal items; this can be linked to the offers and discounted prices of seasonal items. This lack of research studies motivates the current work to investigate the problem of seasonal items' prices as a time series task. We proposed utilizing two different approaches to address this problem, namely, 1) machine learning (ML)-based models and 2) deep learning (DL)-based models. Thus, this research tuned a set of well-known predictive models on a real-life dataset. Those models are ensemble learning-based models, random forest, Ridge, Lasso, and Linear regression. Moreover, two new DL architectures based on gated recurrent unit (GRU) and long short-term memory (LSTM) models are proposed. Then, the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics, where the evaluation includes both numerical and visual comparisons of the examined models. The obtained results show that the ensemble learning models outperformed the classic machine learning-based models (e.g., linear regression and random forest) and the DL-based models.

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Section: Deep Learning Methodsmentioning

confidence: 99%