Past week
All results
- All results
- Verbatim
5 days ago · TCNs can look far back into the sequence without the memory decay problem that LSTMs face. For instance: TCN RMSE: 45 kWh; LSTM RMSE: 50 kWh; ARIMA RMSE: 60 kWh.
4 days ago · To find an accurate open-set gas classification model, we proposed a MSE-TCN, which integrates squeeze-and-excitation residual network (SE-ResNet) internally ...
4 days ago · The results of this experimental study showed that TCN outperformed LSTM in terms of forecasting accuracy. Furthermore, TCN was less sensitive to hyperparameter ...
5 days ago · LSTM (Long Short-Term Memory) networks are a specialized type of recurrent neural network (RNN) designed to effectively capture long-term dependencies in ...
4 days ago · The application of CNN showcased considerable predictive accuracy compared to the Vanilla LSTM, achieving 88.66% accuracy with monthly time steps.
3 days ago · Especially for predictions with a longer time span, LSTM has a significant accuracy advantage [46]. Although the TCN proposed in recent years has a faster ...
4 days ago · LSTM (Long Short-Term Memory): LSTM networks are adept at capturing long-term dependencies in sequential data, making them ideal for time series forecasting.
6 days ago · The TCNForecaster often achieves higher accuracy than standard time series models when there are thousands or more observations in the training history. However ...
7 days ago · Experience with LSTM has shown that stacking recurrent hidden layers with sigmoidal activation units effectively captures the structure of time series data, ...
4 days ago · For example, the GA-TCN-LSTM model proposed by Zhang et al. [14] shows high accuracy in predicting dynamic response at different train speeds. Li et al. [15] ...