diff --git a/docs/source/predictions.rst b/docs/source/predictions.rst
index 6b4db936..556ff06c 100644
--- a/docs/source/predictions.rst
+++ b/docs/source/predictions.rst
@@ -1,27 +1,81 @@
 Predicting Trajectories
 =======================
 
-Predicting trajectories with `traja` can be done with an LSTM neural network
-via :class:`~traja.models.nn.TrajectoryLSTM`.
+Predicting trajectories with `traja` can be done with a recurrent neural network (RNN). `Traja` includes
+the Long Short Term Memory (LSTM), LSTM Autoencoder (LSTM AE) and LSTM Variational Autoencoder (LSTM VAE)
+RNNs. Traja also supports custom RNNs.
+
+To model a trajectory using RNNs, one needs to fit the network to the model. `Traja` includes the MultiTaskRNNTrainer
+that can solve a prediction, classification and regression problem with `traja` DataFrames.
+
+`Traja` also includes a DataLoader that handles `traja` dataframes.
+
+Below is an example with a prediction LSTM:
+via :class:`~traja.models.predictive_models.lstm.LSTM`.
 
 .. code-block:: python
 
     import traja
 
-    df = traja.generate(n=1000)
+    df = traja.datasets.example.jaguar()
+
+.. note::
+    LSTMs work better with data between -1 and 1. Therefore the data loader
+    scales the data. To view the data in the original coordinate system,
+    you need to invert the scaling with the returned `scaler`.
+
+.. code-block:: python
+
+    batch_size = 10 # How many sequences to train every step. Constrained by GPU memory.
+    num_past = 10 # How many time steps from which to learn the time series
+    num_future = 5 # How many time steps to predict
+
 
-Train and visualize predictions
+    data_loaders, scalers = dataset.MultiModalDataLoader(df,
+                                                         batch_size=batch_size,
+                                                         n_past=num_past,
+                                                         n_future=num_future,
+                                                         num_workers=1)
 
 .. note::
 
-    Recommended training is over 5000 epochs. This example only uses 10 epochs for demonstration.
+    The width of the hidden layers and depth of the network are the two main way in which
+    one tunes the performance of the network. More complex datasets require wider and deeper
+    networks. Below are sensible defaults.
 
 .. code-block:: python
 
-    from traja.models.nn import TrajectoryLSTM
+    from traja.models.predictive_models.lstm import LSTM
+    input_size = 2 # Number of input dimensions (normally x, y)
+    output_size = 2 # Same as input_size when predicting
+    num_layers = 2 # Number of LSTM layers. Deeper learns more complex patterns but overfits.
+    hidden_size = 32 # Width of layers. Wider learns bigger patterns but overfits. Try 32, 64, 128, 256, 512
+    dropout = 0.1 # Ignore some network connections. Improves generalisation.
+
+    model = LSTM(input_size=input_size,
+                 hidden_size=hidden_size,
+                 num_layers=num_layers,
+                 output_size=output_size,
+                 dropout=dropout,
+                 batch_size=batch_size,
+                 num_future=num_future)
+
+.. note::
+
+    Recommended training is over 50 epochs. This example only uses 10 epochs for demonstration.
+
+.. code-block:: python
+
+    from traja.models.train import HybridTrainer
+
+    optimizer_type = 'Adam' # Nonlinear optimiser with momentum
+    loss_type = 'huber'
 
-    lstm = TrajectoryLSTM(xy=df.traja.xy, epochs=10)
-    lstm.train()
-    lstm.plot(interactive=True)
+    # Trainer
+    trainer = HybridTrainer(model=model,
+                            optimizer_type=optimizer_type,
+                            loss_type=loss_type)
+    # Train the model
+    trainer.fit(data_loaders, model_save_path, epochs=10, training_mode='forecasting')
 
 .. image:: _static/rnn_prediction.png
\ No newline at end of file