A Quick Example of Time-Series Prediction Using Long Short-Term Memory (LSTM) Networks

Ian Felton
Aug 2, 2019 · 4 min read

The code in the post can be found at https://github.com/gianfelton/12-Month-Forecast-With-LSTM

After seeing a lot of posts where predictions were plotted against test sets (my posts included), I wanted to do a quick demo of actually predicting beyond the time-frame of a dataset. (Although it isn’t shown, RMSE was used to tune parameters.)

Part 1: Building the Model and Comparing Against the Test Set

Let’s start with our imports.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from statsmodels.tools.eval_measures import rmse
from sklearn.preprocessing import MinMaxScaler
from keras.preprocessing.sequence import TimeseriesGenerator
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
import warnings

df = pd.read_csv('AirPassengers.csv')

The next thing we want to do is put the month column in the index.

df.Month = pd.to_datetime(df.Month)
df = df.set_index("Month")

With that finished, we can split our data between the training and testing sets.

train, test = df[:-12], df[-12:]

We’ll need to scale our data.

scaler = MinMaxScaler()
train = scaler.transform(train)
test = scaler.transform(test)

From here, we can create and fit our model.

n_input = 12
n_features = 1
generator = TimeseriesGenerator(train, train, length=n_input, batch_size=6)

I got the technique below from Caner Dabakoglu here on Medium. In it we are doing a few things:

  • create an empty list for each of our 12 predictions
  • create the batch that our model will predict off of
  • save the prediction to our list
  • add the prediction to the end of the batch to be used in the next prediction
pred_list = []

batch = train[-n_input:].reshape((1, n_input, n_features))

for i in range(n_input):
batch = np.append(batch[:,1:,:],[[pred_list[i]]],axis=1)

Now that we have our list of predictions, we need to reverse the scaling we did in the beginning. The code is also creating a dataframe out of the prediction list, which is concatenated with the original dataframe. I did this for plotting. There are many other (better) ways to do this.

df_predict = pd.DataFrame(scaler.inverse_transform(pred_list),                           index=df[-n_input:].index, columns=['Prediction'])

Next, we plot the predictions against the actuals.

plt.figure(figsize=(20, 5))
plt.plot(df_test.index, df_test['AirPassengers'])
plt.plot(df_test.index, df_test['Prediction'], color='r')
plt.legend(loc='best', fontsize='xx-large')

This is good, but what we really need is the ability to predict beyond the time-frame of the dataset. The following code works through this. It is mainly the same code, except where future dates are added on.

Part 2: Predicting Beyond the Dataset

train = df

Here, we create our new dates for the next 12 months.

from pandas.tseries.offsets import DateOffset
add_dates = [df.index[-1] + DateOffset(months=x) for x in range(0,13) ]
future_dates = pd.DataFrame(index=add_dates[1:],columns=df.columns)

The following code is the same, except for the index being set to the future dates.

df_predict = pd.DataFrame(scaler.inverse_transform(pred_list),
index=future_dates[-n_input:].index, columns=['Prediction'])

df_proj = pd.concat([df,df_predict], axis=1)

And now we can check out the results.

plt.figure(figsize=(20, 5))
plt.plot(df_proj.index, df_proj['AirPassengers'])
plt.plot(df_proj.index, df_proj['Prediction'], color='r')
plt.legend(loc='best', fontsize='xx-large')


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Ian Felton

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