A Comparative Analysis of ARIMA, GRU, LSTM and BiLSTM on Financial Time Series Forecasting

Pirani, Muskaan; Thakkar, Paurav; Jivrani, Pranay; Bohara, Mohammed Husain; Garg, Dweepna

doi:10.1109/icdcece53908.2022.9793213

Cited by 44 publications

(21 citation statements)

References 11 publications

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“…In this study, we found that significant improvements in predictive accuracy could be achieved through extensive experimentation with the model's hyperparameters. However, in some studies, the GRU outperformed the LSTM in terms of achieving higher predictive accuracy (see Pirani et al (2022), Salimath et al (2021)). Therefore, this research also examines the accuracy of such an encoder-decoder architecture with GRU and proposes a new model with improved performance.…”

Section: Stacked Ae-lstm and Ae-gru Architecturesmentioning

confidence: 99%

Encoder–Decoder Based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction

Dip Das,

Thulasiram,

Henry

et al. 2024

JRFM

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This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions. To tackle the challenge effectively, novel encoder–decoder architectures, AE-LSTM and AE-GRU, integrating the encoder–decoder principle with LSTM and GRU, are designed. The experimentation involves multiple activation functions and hyperparameter tuning. With extensive experimentation and enhancements applied to AE-LSTM, the proposed AE-GRU architecture still demonstrates significant superiority in forecasting the annual prices of volatile financial assets from the multiple sectors mentioned above. Thus, the novel AE-GRU architecture emerges as a superior choice for price prediction across diverse sectors and fluctuating volatile market scenarios by extracting important non-linear features of financial data and retaining the long-term context from past observations.

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Section: Stacked Ae-lstm and Ae-gru Architecturesmentioning

confidence: 99%

Encoder–Decoder Based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction

Dip Das,

Thulasiram,

Henry

et al. 2024

JRFM

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“…Time series classification has been extensively studied in applied statistics and computational sciences for decades. Many existing machine-learning algorithms and techniques can potentially be applied to PBRTQC to improve current state-of-the-art methods [ 26 - 29 ].…”

Section: Machine Learningmentioning

confidence: 99%

“…The time structure for each dataset is unique and requires exploration of the data and testing different hypothetical time structures to develop the most appropriate statistical model. The rise of neural network-based machine learning models, such as convolutional neural networks, long short-term memory, recurrent neural networks, and attention models, has provided a new direction for solving time series problems [ 28 - 30 ]. Machine learning algorithms eliminate the need for prior knowledge of the time structure as they can automatically identify the best time structure model.…”

Section: Machine Learningmentioning

confidence: 99%

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Next-Generation Patient-Based Real-Time Quality Control Models

Duan,

Zhang,

Liu

et al. 2024

Ann Lab Med

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Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

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“…Furthermore, deep learning networks have emerged as powerful tools for time series forecasting or predicting future data, using techniques such as CNNs, recurrent neural networks (RNNs), and temporal convolutional networks (TCNs) [12]. For example, Pirani [13] explored various RNN-based architectures for financial time series forecasting, demonstrating that models incorporating gated recurrent unit (GRU) layers outperformed other recurrent networks. Likewise, Mahjob [14] used quite similar architectures for energy consumption prediction, and the results showed that the network containing long short-term memory (LSTM) layer in its architectures outperformed other RNN-based models.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest

Esmaeili,

Cassie,

Nguyen

et al. 2023

Bioengineering

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Nanomaterial-based aptasensors serve as useful instruments for detecting small biological entities. This work utilizes data gathered from three electrochemical aptamer-based sensors varying in receptors, analytes of interest, and lengths of signals. Our ultimate objective was the automatic detection and quantification of target analytes from a segment of the signal recorded by these sensors. Initially, we proposed a data augmentation method using conditional variational autoencoders to address data scarcity. Secondly, we employed recurrent-based networks for signal extrapolation, ensuring uniform signal lengths. In the third step, we developed seven deep learning classification models (GRU, unidirectional LSTM (ULSTM), bidirectional LSTM (BLSTM), ConvGRU, ConvULSTM, ConvBLSTM, and CNN) to identify and quantify specific analyte concentrations for six distinct classes, ranging from the absence of analyte to 10 μM. Finally, the second classification model was created to distinguish between abnormal and normal data segments, detect the presence or absence of analytes in the sample, and, if detected, identify the specific analyte and quantify its concentration. Evaluating the time series forecasting showed that the GRU-based network outperformed two other ULSTM and BLSTM networks. Regarding classification models, it turned out signal extrapolation was not effective in improving the classification performance. Comparing the role of the network architectures in classification performance, the result showed that hybrid networks, including both convolutional and recurrent layers and CNN networks, achieved 82% to 99% accuracy across all three datasets. Utilizing short-term Fourier transform (STFT) as the preprocessing technique improved the performance of all datasets with accuracies from 84% to 99%. These findings underscore the effectiveness of suitable data preprocessing methods in enhancing neural network performance, enabling automatic analyte identification and quantification from electrochemical aptasensor signals.

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A Comparative Analysis of ARIMA, GRU, LSTM and BiLSTM on Financial Time Series Forecasting

Cited by 44 publications

References 11 publications

Encoder–Decoder Based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction

Encoder–Decoder Based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction

Next-Generation Patient-Based Real-Time Quality Control Models

Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest

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