# Prediction interval around LSTM time series forecast

Is there a method to calculate the prediction interval (probability distribution) around a time series forecast from an LSTM (or other recurrent) neural network?

Say, for example, I am predicting 10 samples into the future (t+1 to t+10), based on the last 10 observed samples (t-9 to t), I would expect the prediction at t+1 to be more accurate than the prediction at t+10. Typically, one might draw error bars around the prediction to show the interval. With an ARIMA model (under the assumption of normally distributed errors), I can calculate a prediction interval (e.g. 95%) around each predicted value. Can I calculate the same, (or something that relates to the prediction interval) from an LSTM model?

I'm been working with LSTMs in Keras/Python, following lots of examples from machinelearningmastery.com, from which my example code (below) is based on. I'm considering reframing the problem as classification into discrete bins, as that produces a confidence per class, but that seems a poor solution.

There are a couple of similar topics (such as the below), but nothing seems to directly address the issue of prediction intervals from LSTM (or indeed other) neural networks:

https://stats.stackexchange.com/questions/25055/how-to-calculate-the-confidence-interval-for-time-series-prediction

Time series prediction using ARIMA vs LSTM

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from math import sin
from matplotlib import pyplot
import numpy as np

# Build an LSTM network and train
def fit_lstm(X, y, batch_size, nb_epoch, neurons):
X = X.reshape(X.shape[0], 1, X.shape[1]) # add in another dimension to the X data
y = y.reshape(y.shape[0], y.shape[1])      # but don't add it to the y, as Dense has to be 1d?
model = Sequential()
for i in range(nb_epoch):
model.fit(X, y, epochs=1, batch_size=batch_size, verbose=1, shuffle=False)
model.reset_states()
return model

# Configuration
n = 5000    # total size of dataset
SLIDING_WINDOW_LENGTH = 30
SLIDING_WINDOW_STEP_SIZE = 1
batch_size = 10
test_size = 0.1 # fraction of dataset to hold back for testing
nb_epochs = 100 # for training
neurons = 8 # LSTM layer complexity

# create dataset
#raw_values = [sin(i/2) for i in range(n)]  # simple sine wave
raw_values = [sin(i/2)+sin(i/6)+sin(i/36)+np.random.uniform(-1,1) for i in range(n)]  # double sine with noise
#raw_values = [(i%4) for i in range(n)] # saw tooth

all_data = np.array(raw_values).reshape(-1,1) # make into array, add anothe dimension for sci-kit compatibility

# data is segmented using a sliding window mechanism
all_data_windowed = [np.transpose(all_data[idx:idx+SLIDING_WINDOW_LENGTH]) for idx in np.arange(0,len(all_data)-SLIDING_WINDOW_LENGTH, SLIDING_WINDOW_STEP_SIZE)]
all_data_windowed = np.concatenate(all_data_windowed, axis=0).astype(np.float32)

# split data into train and test-sets
# round datasets down to a multiple of the batch size
test_length = int(round((len(all_data_windowed) * test_size) / batch_size) * batch_size)
train, test = all_data_windowed[:-test_length,:], all_data_windowed[-test_length:,:]
train_length = int(np.floor(train.shape[0] / batch_size)*batch_size)
train = train[:train_length,...]

half_size = int(SLIDING_WINDOW_LENGTH/2) # split the examples half-half, to forecast the second half
X_train, y_train = train[:,:half_size], train[:,half_size:]
X_test, y_test = test[:,:half_size], test[:,half_size:]

# fit the model
lstm_model = fit_lstm(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epochs, neurons=neurons)

# forecast the entire training dataset to build up state for forecasting
X_train_reshaped = X_train.reshape(X_train.shape[0], 1, X_train.shape[1])
lstm_model.predict(X_train_reshaped, batch_size=batch_size)

# predict from test dataset
X_test_reshaped = X_test.reshape(X_test.shape[0], 1, X_test.shape[1])
yhat = lstm_model.predict(X_test_reshaped, batch_size=batch_size)

#%% Plot prediction vs actual

x_axis_input = range(half_size)
x_axis_output = [x_axis_input[-1]] + list(half_size+np.array(range(half_size)))

fig = pyplot.figure()
line1, = ax.plot(x_axis_input,np.zeros_like(x_axis_input), 'r-')
line2, = ax.plot(x_axis_output,np.zeros_like(x_axis_output), 'o-')
line3, = ax.plot(x_axis_output,np.zeros_like(x_axis_output), 'g-')
ax.set_xlim(np.min(x_axis_input),np.max(x_axis_output))
ax.set_ylim(-4,4)
pyplot.legend(('Input','Actual','Predicted'),loc='upper left')
pyplot.show()

# update plot in a loop
for idx in range(y_test.shape[0]):

sample_input = X_test[idx]
sample_truth = [sample_input[-1]] + list(y_test[idx]) # join lists
sample_predicted = [sample_input[-1]] + list(yhat[idx])

line1.set_ydata(sample_input)
line2.set_ydata(sample_truth)
line3.set_ydata(sample_predicted)
fig.canvas.draw()
fig.canvas.flush_events()

pyplot.pause(.25)


Directly, this is not possible. However, if you model it in a different way you can get out confidence intervals. You could instead of a normal regression approach it as estimating a continuous probability distribution. By doing this for every step you can plot your distribution. Ways to do this are Kernel Mixture Networks (https://janvdvegt.github.io/2017/06/07/Kernel-Mixture-Networks.html, disclosure, my blog) or Density Mixture Networks (http://www.cedar.buffalo.edu/~srihari/CSE574/Chap5/Chap5.7-MixDensityNetworks.pdf), the first uses kernels as base and estimates a mixture over these Kernels and the second one estimates a mixture of distributions, including the parameters of each of the distributions. You use the log likelihood for training the model.

Another option for modeling the uncertainty is to use dropout during training and then also during inference. You do this multiple times and every time you get a sample from your posterior. You don't get distributions, only samples, but it's the easiest to implement and it works very well.

In your case you have to think about the way you generate t+2 up to t+10. Depending on your current setup you might have to sample from the previous time step and feed that for the next one. That doesn't work very well with the first approach, nor with the second. If you have 10 outputs per time step (t+1 up to t+10) then all of these approaches are more clean but a bit less intuitive.

• Using mixture networks is interesting, I will try to implement that. There is some solid research on using dropout here: arxiv.org/abs/1709.01907 and arxiv.org/abs/1506.02142
– 4Oh4
Nov 6, 2017 at 16:58
• A note for the dropout, you can actually calculate the variance of prediction of monte carlo dropout, and use that as quantification of uncertainty Feb 5, 2019 at 23:02
• That is true @CharlesChow but that is a poor way to construct a confidence interval in this context. It would be better to sort the values and use quantiles due to the potentially very skewed distribution. Feb 7, 2019 at 12:31
• Agree @JanvanderVegt , but you still can estimate the statistics of MC dropout without the assumption of output distribution, I mean you can also use percentile or bootstrapping to construct the CI of MC dropout Feb 7, 2019 at 21:55

Conformal Prediction as a buzz word might be interesting for you because it works under many conditions - in particular it does not need normal distributed error and it works for almost any machine learning model.

Two nice introductions are given by Scott Locklin and Henrik Linusson.

I am going to diverge a little bit and argue that calculation confidence interval is in practice is usually not a valuable thing to do. The reason is there is always a whole bunch of assumptions you need to make. Even for the simplest linear regression, you need to have

• Linear relationship.
• Multivariate normality.
• No or little multicollinearity.
• No auto-correlation.
• Homoscedasticity.

A much more pragmatic approach is to do a Monte Carlo simulation. If you already know or willing to make of assumption around the distribution of your input variables, take a whole bunch of sample and feed it to you LSTM, now you can empirically calculate your "confidence interval".

Yes, you can. The only thing you need to change is the loss function. Implement the loss function used in quantile regression and integrate it. Also, you want to take a look at how you evaluate those intervals. For that, I would use ICP, MIL and RMIL metrics.