Importance of Data Standardization Methods on Stock Indices Prediction Accuracy

Kumari, Binita; Swarnkar, Tripti

doi:10.1007/978-981-15-1081-6_26

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…However, data standardization can accelerate the training process and reduce the possibility of the model falling into the local optimal solution. Many researchers have reported that data normalization plays an important role in improving algorithm performance, such as in medical data classifcation [39,40] and stock market prediction [41]. Terefore, it is necessary to normalize the data and limit the data distribution interval.…”

Section: Partial Normalizationmentioning

confidence: 99%

STP-Net: A Signal Prediction Method of Laminar Decompression Robot Based on Swin Transformer

Zhang

et al. 2023

International Journal of Intelligent Systems

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Spinal surgery robots have a great application value in laminar decompression surgery. For a safe surgery, the robot needs to accurately identify the cutting state of the lamina. Therefore, it is very important to deal with various sensing signals in the form of a time series. However, various state recognition algorithms proposed so far cannot completely avoid cutting through the lamina, leaving hidden dangers for nerve thermal damage caused by high-temperature liquid splashing. We propose a long time series prediction algorithm called STP-Net, which combined with the existing algorithms for recognizing the lamina cutting state can stop the lamina cutting in advance, thus preserving a thin inner layer of cortical bone and blocking high-temperature liquid splashing. STP-Net has the following advantages: (1) Dimension reduction is first performed on Swin (shifted window) Transformer to obtain 1D Swin Transformer, which was used to develop STP-Net for long time series prediction, which not only has high accuracy but also shows a linear relationship between computational complexity and input sequence length L, namely, O L . (2) The token merging layer is proposed and applied to the encoder of STP-Net, which reduces the computation cost and improves the global information extraction capability of the algorithm. (3) STP-Net uses a generative decoder to output the prediction sequence directly through a single operation, which not only has high efficiency but also avoids the accumulation of errors. Taking force signals as an example, when 400 numbers are used to predict 100 numbers, the average mean square error (MSE) and mean absolute error (MAE) of standard STP-Net are 1.22 × 10−3 and 2.42 × 10−2, respectively. STP-Net, combined with the existing method for recognizing the lamina cutting status, can stop the robot from cutting in advance in 87.14% of the cases. In addition, the results of practical lamina cutting experiments confirmed the effectiveness of STP-Net.

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Section: Partial Normalizationmentioning

confidence: 99%

STP-Net: A Signal Prediction Method of Laminar Decompression Robot Based on Swin Transformer

Zhang

et al. 2023

International Journal of Intelligent Systems

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“…For instance, data normalization is a transformation method in which each column's data are transmitted in a specified range (usually between zero and one). [ 86 Several studies have been conducted on the performance of different data transformation methods that directly affects the predictive power of ML models. For example, NOREVA is a web‐based software that is used to evaluate the performance of different normalization methods on metabolomics data.…”

Section: Ml: a Data‐driven Approachmentioning

confidence: 99%

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Khaleghi

Savizi

Lewis

et al. 2021

Biotechnology Journal

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Recent noteworthy advances in developing high‐performing microbial and mammalian strains have enabled the sustainable production of bio‐economically valuable substances such as bio‐compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass‐production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge‐ and data‐driven modeling methods have received much attention. Constraint‐based modeling (CBM) is a knowledge‐driven mathematical approach that has been widely used in fermentation analysis and optimization due to its ability to predict the cellular phenotype from genotype through high‐throughput means. On the other hand, machine learning (ML) is a data‐driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint‐based models in a reciprocal manner when one is used as a pre‐step of another. As a result, a more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters.

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“…[13][14][15][16][17] are few of the studies that have been done using Artificial Neural Networks to model stock prices. [18,5] include several more papers on using neural networks to predict stock market volatility. The results obtained through the use of ANNs are superior to those obtained through the use of linear and logical regression models [19,7].…”

Section: Literature Reviewmentioning

confidence: 99%

Stock movement prediction using hybrid normalization technique and artificial neural network

Kumari¹,

Swarnkar²

2021

IJATEE

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Importance of Data Standardization Methods on Stock Indices Prediction Accuracy

Cited by 8 publications

References 18 publications

STP-Net: A Signal Prediction Method of Laminar Decompression Robot Based on Swin Transformer

STP-Net: A Signal Prediction Method of Laminar Decompression Robot Based on Swin Transformer

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Stock movement prediction using hybrid normalization technique and artificial neural network

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