The good performance of the Vanilla RNN, which does not integrate the “long” aspect of the LSTM algorithm, implies that the time series follows a pattern that does not require much of a long-term memory.
Read moreIs RNN good for time series?
The good performance of the Vanilla RNN, which does not integrate the “long” aspect of the LSTM algorithm, implies that the time series follows a pattern that does not require much of a long-term memory.
Read moreIs there anything better than LSTM?
Temporal convolutional network (TCN) “outperform canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory”. Note 4: Related to this topic, is the fact that we know little of how our human brain learns and remembers sequences.
Read moreCan LSTM be used for time series prediction?
LSTM are useful for making predictions, classification and processing sequential data . We use many kinds of LSTM for different purposes or for different specific types of time series forecasting.
Read moreIs time series used for forecasting?
What is time series forecasting? Time series forecasting is the process of analyzing time series data using statistics and modeling to make predictions and inform strategic decision-making .
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