Giant Left Ventricular Apical Pseudoaneurysm

Gourav, Krishna Prasad; Raja, Javid; Negi, Sunder; Maramreddy, Revanth

doi:10.22541/au.160029865.50926988

Cited by 1 publication

(1 citation statement)

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“…However, these models, constrained by fixed mathematical formulas, struggle to adequately express the complex features of time series, posing challenges in accurately predicting them. While classical methods like ARIMA models [15,16] and exponential smoothing [17,18] have been utilized in many studies with certain achievements, they often focus on a single time series, overlooking the correlations between sequences and encountering limitations in handling complex time patterns, such as long-term dependencies and non-linear relationships.…”

Section: Related Workmentioning

confidence: 99%

BiLSTM-MLAM: A Multi-Scale Time Series Prediction Model for Sensor Data Based on Bi-LSTM and Local Attention Mechanisms

Fan,

Tang,

Guo

et al. 2024

Sensors

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This paper introduces BiLSTM-MLAM, a novel multi-scale time series prediction model. Initially, the approach utilizes bidirectional long short-term memory to capture information from both forward and backward directions in time series data. Subsequently, a multi-scale patch segmentation module generates various long sequences composed of equal-length segments, enabling the model to capture data patterns across multiple time scales by adjusting segment lengths. Finally, the local attention mechanism enhances feature extraction by accurately identifying and weighting important time segments, thereby strengthening the model’s understanding of the local features of the time series, followed by feature fusion. The model demonstrates outstanding performance in time series prediction tasks by effectively capturing sequence information across various time scales. Experimental validation illustrates the superior performance of BiLSTM-MLAM compared to six baseline methods across multiple datasets. When predicting the remaining life of aircraft engines, BiLSTM-MLAM outperforms the best baseline model by 6.66% in RMSE and 11.50% in MAE. In the LTE dataset, it achieves RMSE improvements of 12.77% and MAE enhancements of 3.06%, while in the load dataset, it demonstrates RMSE enhancements of 17.96% and MAE improvements of 30.39%. Additionally, ablation experiments confirm the positive impact of each module on prediction accuracy. Through segment length parameter tuning experiments, combining different segment lengths has resulted in lower prediction errors, affirming the effectiveness of the multi-scale fusion strategy in enhancing prediction accuracy by integrating information from multiple time scales.

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