Below is an example that sets up time series data to train an LSTM. The model output is nonsense as I only set it up to demonstrate how to build the model.
import pandas as pd
import numpy as np
# Get some time series data
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/timeseries.csv")
df.head()
Time series dataframe:
Date A B C D E F G
0 2008-03-18 24.68 164.93 114.73 26.27 19.21 28.87 63.44
1 2008-03-19 24.18 164.89 114.75 26.22 19.07 27.76 59.98
2 2008-03-20 23.99 164.63 115.04 25.78 19.01 27.04 59.61
3 2008-03-25 24.14 163.92 114.85 27.41 19.61 27.84 59.41
4 2008-03-26 24.44 163.45 114.84 26.86 19.53 28.02 60.09
You can build put inputs into a vector and then use pandas .cumsum() function to build the sequence for the time series:
# Put your inputs into a single list
df['single_input_vector'] = df[input_cols].apply(tuple, axis=1).apply(list)
# Double-encapsulate list so that you can sum it in the next step and keep time steps as separate elements
df['single_input_vector'] = df.single_input_vector.apply(lambda x: [list(x)])
# Use .cumsum() to include previous row vectors in the current row list of vectors
df['cumulative_input_vectors'] = df.single_input_vector.cumsum()
The output can be set up in a similar way, but it will be a single vector instead of a sequence:
# If your output is multi-dimensional, you need to capture those dimensions in one object
# If your output is a single dimension, this step may be unnecessary
df['output_vector'] = df[output_cols].apply(tuple, axis=1).apply(list)
The input sequences have to be the same length to run them through the model, so you need to pad them to be the max length of your cumulative vectors:
# Pad your sequences so they are the same length
from keras.preprocessing.sequence import pad_sequences
max_sequence_length = df.cumulative_input_vectors.apply(len).max()
# Save it as a list
padded_sequences = pad_sequences(df.cumulative_input_vectors.tolist(), max_sequence_length).tolist()
df['padded_input_vectors'] = pd.Series(padded_sequences).apply(np.asarray)
Training data can be pulled from the dataframe and put into numpy arrays. Note that the input data that comes out of the dataframe will not make a 3D array. It makes an array of arrays, which is not the same thing.
You can use hstack and reshape to build a 3D input array.
# Extract your training data
X_train_init = np.asarray(df.padded_input_vectors)
# Use hstack to and reshape to make the inputs a 3d vector
X_train = np.hstack(X_train_init).reshape(len(df),max_sequence_length,len(input_cols))
y_train = np.hstack(np.asarray(df.output_vector)).reshape(len(df),len(output_cols))
To prove it:
>>> print(X_train_init.shape)
(11,)
>>> print(X_train.shape)
(11, 11, 6)
>>> print(X_train == X_train_init)
False
Once you have training data you can define the dimensions of your input layer and output layers.
# Get your input dimensions
# Input length is the length for one input sequence (i.e. the number of rows for your sample)
# Input dim is the number of dimensions in one input vector (i.e. number of input columns)
input_length = X_train.shape[1]
input_dim = X_train.shape[2]
# Output dimensions is the shape of a single output vector
# In this case it's just 1, but it could be more
output_dim = len(y_train[0])
Build the model:
from keras.models import Model, Sequential
from keras.layers import LSTM, Dense
# Build the model
model = Sequential()
# I arbitrarily picked the output dimensions as 4
model.add(LSTM(4, input_dim = input_dim, input_length = input_length))
# The max output value is > 1 so relu is used as final activation.
model.add(Dense(output_dim, activation='relu'))
model.compile(loss="mean_squared_error",
optimizer="sgd",
metrics=['accuracy'])
Finally you can train the model and save the training log as history:
# Set batch_size to 7 to show that it doesn't have to be a factor or multiple of your sample size
history = model.fit(X_train, y_train,
batch_size=7, nb_epoch=3,
verbose = 1)
Output:
Epoch 1/3
11/11 [==============================] - 0s - loss: 3498.5756 - acc: 0.0000e+00
Epoch 2/3
11/11 [==============================] - 0s - loss: 3498.5755 - acc: 0.0000e+00
Epoch 3/3
11/11 [==============================] - 0s - loss: 3498.5757 - acc: 0.0000e+00
That’s it. Use model.predict(X) where X is the same format (other than the number of samples) as X_train in order to make predictions from the model.