I just built this LSTM neural network with Keras

    import numpy as np
    import pandas as pd 
    from sklearn import preprocessing
    from keras.layers.core import Dense, Dropout, Activation
    from keras.activations import linear
    from keras.layers.recurrent import LSTM
    from keras.models import Sequential
    from matplotlib import pyplot

    #read and prepare data from datafile
    data_file_name = "DailyDemand.csv"
    data_csv = pd.read_csv(data_file_name, delimiter = ';',header=None, usecols=[1,2,3,4,5])
    yt = data_csv[1:]
    data = yt
    data.columns = ['MoyenneTransactHier', 'MaxTransaction', 'MinTransaction','CountTransaction','Demand']
    # print (data.head(10))
    pd.options.display.float_format = '{:,.0f}'.format
    data = data.dropna ()
    cols=['MoyenneTransactHier', 'MaxTransaction', 'MinTransaction','CountTransaction']

    #scaling data
    scaler_x = preprocessing.MinMaxScaler(feature_range =(-1, 1))
    x = np.array(x).reshape ((len(x),4 ))
    x = scaler_x.fit_transform(x)
    scaler_y = preprocessing.MinMaxScaler(feature_range =(-1, 1))
    y = np.array(y).reshape ((len(y), 1))
    y = scaler_y.fit_transform(y)
    print("longeur de y",len(y))
    # Split train and test data
    train_end = 80
    x_train=x[0: train_end ,]
    x_test=x[train_end +1: ,]
    y_train=y[0: train_end]
    y_test=y[train_end +1:] 
    x_train=x_train.reshape(x_train.shape +(1,))
    x_test=x_test.reshape(x_test.shape + (1,))

    print("Data well prepared")
    print ('x_train shape ', x_train.shape)
    print ('y_train', y_train.shape)

    #Design the model - LSTM Network
    seed = 2016
    fit1 = Sequential ()
        output_dim = 4,
        input_shape =(4, 1)))
    fit1.add(Dense(output_dim =1))
    #rmsprop or sgd
    batchsize = 1
    #train the model
    fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =20, shuffle=True)

    print(fit1.summary ())

    #Model error
    score_train = fit1.evaluate(x_train ,y_train ,batch_size =batchsize)
    score_test = fit1.evaluate(x_test , y_test ,batch_size =batchsize)
    print("in  train  MSE = ",round(score_train,4))
    print("in test  MSE = ",round(score_test ,4))

    #Make prediction
    pred1 = scaler_y.inverse_transform(np.array(pred1).reshape ((len(pred1), 1)))
    real_test = scaler_y.inverse_transform(np.array(y_test).reshape ((len(y_test), 1))).astype(int)

    #save prediction
    testData = pd.DataFrame(real_test)
    preddData = pd.DataFrame(pred1)
    dataF = pd.concat([testData,preddData], axis=1)
    dataF.columns =['Real demand','Predicted Demand']

    pyplot.plot(pred1, label='Forecast')

then it generates this result: Prediction on the test data

After building and training a good model on the historical data, I don't know how I can generate the prediction for future values? For example the demand of the next 10 days. Data are daily.

this is an example of how the data is shaped

NB: this is an example of how the data is shaped, the green is the label and the yellow one are the features.
after dropna() (delete null values) it remains 100 data rows, I've used 80 in the training and the 20 in the test.

  • $\begingroup$ When you break your time series up, how many instances do you have? $\endgroup$
    – JahKnows
    Commented Apr 24, 2018 at 16:36
  • $\begingroup$ Sorry sir, i dont got you, can you explain more? thank you $\endgroup$
    – Nbenz
    Commented Apr 24, 2018 at 23:56
  • $\begingroup$ After having restructured your data for a forecasting problem, how many lines of examples do you have? $\endgroup$
    – JahKnows
    Commented Apr 25, 2018 at 1:56
  • $\begingroup$ Can you give me a single time sequence of points and I will show you how to do forecasting with them. $\endgroup$
    – JahKnows
    Commented Apr 25, 2018 at 1:57
  • $\begingroup$ You can check again the question i've edited it by adding an example of the data format and shape. thanks $\endgroup$
    – Nbenz
    Commented Apr 25, 2018 at 2:47

1 Answer 1

This answer goes a little bit in a different direction, but I hope it still answers your question. It uses the idea of a rolling forecast/prediction.

Because you use the word horizon, I will assume you mean that you would like to predict 10 days into the future at a given time step. There are a few ways of doing this. With this kind of time-series problem, it is common to make the assumption that only a certain history will influence the next few time steps (neglecting seasonal effects).

Example in words:

So in your case, you might use e.g. the previous 60 days, and predict the next 10. Taking your 100 rows of data as an example, this means you can actually make (100 - 60 - 9) = 31 predictions, each prediction of 10 time steps ahead (we will need these 31 predictive_blocks later). From 100 rows we lose the first 60 to fit the first model. Of the remaining 40 rows of data, we can predict 10 steps ahead (rows 61-70), then we shift the whole thing one row further and repeat. The last prediction of 10 future points would be for rows 91-100. After that we cannot predict 10 steps anymore, so we stop - and this is why we have to subtract that extra 9. [There are of course ways to continue making prediction, as to use all the data]

Example with a thousand words:

Let me paint the picture; to help explain the idea of a shifting window prediction.

For each train set (e.g. from t=0 to t=5 in red - train set 1), you want to predict the following H time steps (corresponding to t=6 in orange - test set 1). In this, your horizon is simply one i.e. H=1.

Basic sketch of a rolling out-of-sample forecast

From what I understand, you would like to predict the next 10 days, meaning you need H=10.

In order to try this with your example, I think you will need to make two changes.

Change #1

The shape of your train and test sets will need to match the new horizon. Each sample of your model input (the x_train and x_test can stay the same as before. However, each sample in your test set will have to contain the next H=10 values of the label, not just a single value.

Here is a rough example of how you might do this:

# Define our horizon
H = 10

# Create data split, using values from my example above
window_size = 60
num_pred_blocks = 31    # as computed above

# Loop over the train and test samples to create the sliding window sets
x_train = []
y_train = []
for i in range(num_pred_blocks):
    x_train_block = x_train[i:(i + window_size)]    # 31 blocks of 60 * num-columns
    y_train_block = y_train[(i + window_size):(i + window_size + H)]    # 31 blocks of 10 * 1

Because you are doing out-of-sample testing, your predictions are already interesting to look analyse. Once this runs, you can then create the equivalent test datasets with the new data you mentioned.

Without knowing your data too well, I don't know if your should be predicting the y-values of the same row as the input, or of the following row. Additionally, depending on your data, you could be including the past values of y in each of the x_train blocks. In this case you'd simply swap x for the whole table i.e. data[cols], where new_cols = ['Demand'] + cols.

Change #2

You will need to make the model reflect this horizon, by forcing it to output H values.

Here is an example of how to specify the model:

# Define our horizon
H = 10

# Create the model using the parameterised horizon
fit1 = Sequential ()
fit1.add(LSTM(output_dim = 4, activation='tanh', input_shape =(4, 1)))
fit1.add(Dense(output_dim=30, activation='sigmoid')
fit1.add(Dense(output_dim=H))    # our horizon is produced!

Note: In your model specification, you don't need to add the final linear Activation, as the preceding Dense layer by default includes a linear activation. See the excellent documentation here.

This is a big topic and there are many things that you could try out. I agree with the comments on your question, that you will need a lot more data to allow an RNN to make a meaning representation of the model.

If you are not just doing this to learn about LSTMs etc., another practical approach might be to look into simpler time-series models such as an ARIMA model (do not be intimidated by the complicated name - it is much simpler than an LSTM). Such models can be constructed quite easily with Python, using the statsmodels package, which has a nice implementation.


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