How are recurrent neural networks used in predictive modeling?
Unlike regression predictive modeling, time series also adds the complexity of a sequence dependence among the input variables. A powerful type of neural network designed to handle sequence dependence is called recurrent neural networks. The Long Short-Term Memory network or LSTM network is a type of recurrent neural network used
Which is the best neural network for time series?
A Recurrent Neural Network (RNN) is a type of neural network well-suited to time series data. RNNs process a time series step-by-step, maintaining an internal state from time-step to time-step. For more details, read the text generation tutorial or the RNN guide .
What kind of problem is time series prediction?
Time series prediction problems are a difficult type of predictive modeling problem. Unlike regression predictive modeling, time series also adds the complexity of a sequence dependence among the input variables. A powerful type of neural network designed to handle sequence dependence is called recurrent neural networks.
Is the recurrent neural network a fully connected network?
In programming terms this is like running a fixed program with certain inputs and some internal variables. The simplest recurrent neural network can be viewed as a fully connected neural network if we unroll the time axes. In this univariate case only two weights are involved.
Why are neural networks useful for time series?
This general capability is valuable for time series for a number of reasons. Robust to Noise. Neural networks are robust to noise in input data and in the mapping function and can even support learning and prediction in the presence of missing values.
How does time series forecasting with RNNs work?
On a high level, this model utilize s pretty standard sequence-to-sequence recurrent neural network architecture. Its inputs are past values of the predicted time series concatenated with other driving time series values (optional) and timestamp embeddings (optional).