Time series prediction with multiple sequences input - LSTM - 1

multi-ts-lstm.py

# Time Series Testing 
import keras.callbacks
from keras.models import Sequential  
from keras.layers.core import Dense, Activation, Dense, Dropout
from keras.layers.recurrent import LSTM

# Call back to capture losses 
class LossHistory(keras.callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.losses = []

    def on_batch_end(self, batch, logs={}):
        self.losses.append(logs.get('loss'))
        
# You should get data frames with prices somewhere, e.g. on Quandl - implementation is up to you
# merge data frames
merged = df1.merge(df2, left_index=True, right_index=True, how='inner').dropna()

# data prep
# use 100 days of historical data to predict 10 days in the future
data = merged.values
examples = 100
y_examples = 10
nb_samples = len(data) - examples - y_examples

# input - 2 features
input_list = [np.expand_dims(np.atleast_2d(data[i:examples+i,:]), axis=0) for i in xrange(nb_samples)]
input_mat = np.concatenate(input_list, axis=0)

# target - the first column in merged dataframe
target_list = [np.atleast_2d(data[i+examples:examples+i+y_examples,0]) for i in xrange(nb_samples)]
target_mat = np.concatenate(target_list, axis=0)

# set up model
trials = input_mat.shape[0]
features = input_mat.shape[2]
hidden = 64
model = Sequential()
model.add(LSTM(hidden, input_shape=(examples, features)))
model.add(Dropout(.2))
model.add(Dense(y_examples))
model.add(Activation('linear'))
model.compile(loss='mse', optimizer='rmsprop')

# Train
history = LossHistory()
model.fit(input_mat, target_mat, nb_epoch=100, batch_size=400, callbacks=[history])

Dear lukovkin, Suppose I have multiple time series as input and I need to predict all these time series at once for the next 10 days, how should I reshape the input and target datasets?
Regards

Hi,
Could you please tell me how to predict the next 10 days in future? (i.e.) the steps after model.fit and how to evaluate this model.
Also is it possible to do multiple sequences input with Stateful LSTM?

Thanks in advance.

u need tu put a Dense layer as output with 10 units, and train your network with the same structure N-imputs and 10 outputs values

Hello!
Would you like to help me?
I am stuck on a problem, how to fit a multiple input CNN or LSTM model.
I have a time series data divided (split) into train, test, and validation
I want to give the same training data to two different CNN models and concatenate them. I have built the model as shown in the figure, input shape =168,23

My network is;

but I am not getting how to fit this model.
once I tried to fit the model I face the error

@sarahboufelja @bv123 @kascesar @Sudarsan9966 will be waiting for your kind reply.
Thank you