T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis

Liu, Minhao; Zeng, Ailing; Lai, Qiuxia; Xu, Qiang

doi:10.48550/arxiv.2012.05456

Cited by 1 publication

(1 citation statement)

References 33 publications

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“…Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU) models, with their inherent ability to capture temporal dependencies, are specifically tailored to address the nuances of time series data. Additionally, sequence-to-sequence (Seq2seq) models [13] and WaveNet [14] have gained prominence for their effectiveness in forecasting. CNN model is often used to solve the problem of image classification.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

MAGRU: Multi-layer Attention with GRU for Logistics Warehousing Demand Prediction

2024

KSII TIIS

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Warehousing demand prediction is an essential part of the supply chain, providing a fundamental basis for product manufacturing, replenishment, warehouse planning, etc. Existing forecasting methods cannot produce accurate forecasts since warehouse demand is affected by external factors such as holidays and seasons. Some aspects, such as consumer psychology and producer reputation, are challenging to quantify. The data can fluctuate widely or do not show obvious trend cycles. We introduce a new model for warehouse demand prediction called MAGRU, which stands for Multi-layer Attention with GRU. In the model, firstly, we perform the embedding operation on the input sequence to quantify the external influences; after that, we implement an encoder using GRU and the attention mechanism. The hidden state of GRU captures essential time series. In the decoder, we use attention again to select the key hidden states among all-time slices as the data to be fed into the GRU network. Experimental results show that this model has higher accuracy than RNN, LSTM, GRU, Prophet, XGboost, and DARNN. Using mean absolute error (MAE) and symmetric mean absolute percentage error(SMAPE) to evaluate the experimental results, MAGRU's MAE, RMSE, and SMAPE decreased by 7.65%, 10.03%, and 8.87% over GRU-LSTM, the current best model for solving this type of problem.

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Section: Machine Learning Methodsmentioning

confidence: 99%

MAGRU: Multi-layer Attention with GRU for Logistics Warehousing Demand Prediction

2024

KSII TIIS

View full text Add to dashboard Cite

show abstract

T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis

Cited by 1 publication

References 33 publications

MAGRU: Multi-layer Attention with GRU for Logistics Warehousing Demand Prediction

MAGRU: Multi-layer Attention with GRU for Logistics Warehousing Demand Prediction

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