Machine Learning and Deep Learning for Predicting Indoor and Outdoor IoT Temperature Monitoring Systems

Lestari, Nur Indah; Salah, Ahmad; Mohamed, Mohamed Ali; Fathalla, Ahmed; Bekhit, Mahmoud

doi:10.1007/978-3-030-95987-6_13

Cited by 1 publication

(1 citation statement)

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“…DL techniques, in particular recurrent neural networks (RNNs), have been shown to be effective in a variety of applications, one of which is time series forecasting, where they have been utilized [2,6,15]. Diverse applications, including time series forecasting, have demonstrated the effectiveness of DL approaches.…”

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%