What is better than LSTM for time series?

ARIMA yields better results in forecasting short term, whereas LSTM yields better results for long term modeling. Traditional time series forecasting methods (ARIMA) focus on univariate data with linear relationships and fixed and manually-diagnosed temporal dependence.

Is LSTM and RNN same?

LSTM networks are a type of RNN that uses special units in addition to standard units. LSTM units include a ‘memory cell’ that can maintain information in memory for long periods of time. A set of gates is used to control when information enters the memory, when it’s output, and when it’s forgotten.

Can lag observations be used as time steps for an LSTM?

The Long Short-Term Memory (LSTM) network in Keras supports time steps. This raises the question as to whether lag observations for a univariate time series can be used as time steps for an LSTM and whether or not this improves forecast performance.

How to transform the data before fitting an LSTM model?

Before we can fit an LSTM model to the dataset, we must transform the data. The following three data transforms are performed on the dataset prior to fitting a model and making a forecast. Transform the time series data so that it is stationary. Specifically, a lag=1 differencing to remove the increasing trend in the data.

Can LSTMs be used with long input sequences?

But LSTMs can be challenging to use when you have very long input sequences and only one or a handful of outputs. This is often called sequence labeling, or sequence classification.

How do you train a time series model?

Make the identity of the agent one of the features, and train on all data. Probably train on a mini-batch of eg 128 agents at at time: run through the time-series from start to finish for those 128 agents, then select a new mini-batch of agents. For each mini-batch, run a slice of say 50 timesteps, then backprop.