Concatenating a time-series neural net with a feedforward neural net

Consider the following example problem:

# dummy data for a SO question
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
from keras.models import Model
from keras.layers import Input, Conv1D, Dense
from keras.optimizers import Adam, SGD

time = np.array(range(100))
brk = np.array((time>40) & (time < 60)).reshape(100,1)
B = np.array([5, -5]).reshape(1,2)
np.dot(brk, B)
y = np.c_[np.sin(time), np.sin(time)] + np.random.normal(scale = .2, size=(100,2))+ np.dot(brk, B)



plt.clf()
plt.plot(time, y[:,0])
plt.plot(time, y[:,1])

You've got N time series, and they've got one component that follows a common process, and another component that is idiosyncratic to the series itself. Assume for simplicity that you know a priori that the bump is between 40 and 60, and you want to model it simultaneously with the sinusoidal component.

A TCN does a good job on the common component, but it can't get the series-idiosyncratic component:

# time series model
n_filters = 10
filter_width = 3
dilation_rates = [2**i for i in range(7)] 
inp = Input(shape=(None, 1))
x = inp
for dilation_rate in dilation_rates:
    x = Conv1D(filters=n_filters,
               kernel_size=filter_width, 
               padding='causal',
               activation = "relu",
               dilation_rate=dilation_rate)(x)
x = Dense(1)(x)
model = Model(inputs = inp, outputs = x)
model.compile(optimizer = Adam(), loss='mean_squared_error')
model.summary()

X_train = np.transpose(np.c_[time, time]).reshape(2,100,1)
y_train = np.transpose(y).reshape(2,100,1)

history = model.fit(X_train, y_train,
                batch_size=2,
                epochs=1000,
                verbose = 0)
yhat = model.predict(X_train)
plt.clf()
plt.plot(time, y[:,0])
plt.plot(time, y[:,1])

plt.plot(time, yhat[0,:,:])
plt.plot(time, yhat[1,:,:])

On the other hand, a basic linear regression with N outputs (here implemented in Keras) is perfect for the idiosyncratic component:

inp1 = Input((1,))
x1 = inp1
x1 = Dense(2)(x1)
model1 = Model(inputs = inp1, outputs = x1)
model1.compile(optimizer = Adam(), loss='mean_squared_error')
model1.summary()

brk_train = brk
y_train = y
history = model1.fit(brk_train, y_train,
                batch_size=100,
                epochs=6000, verbose = 0)
yhat1 = model1.predict(brk_train)
plt.clf()
plt.plot(time, y[:,0])
plt.plot(time, y[:,1])
plt.plot(time, yhat1[:,0])
plt.plot(time, yhat1[:,1])

I want to use keras to jointly estimate the time series component and the idiosyncratic component. The major problem is that feed-forward networks (which linear regression is a special case of) take shape batch_size x dims while time series networks take dimension batch_size x time_steps x dims.

Because I want to jointly estimate the idiosyncratic part of the model (the linear regression part) together with the time series part, I'm only ever going to batch-sample whole time-series. Which is why I specified batch_size = time_steps for model 1.

But in the static model, what I'm really doing is modeling my data as time_steps x dims.

I have tried to re-cast the feed-forward model as a time-series model, without success. Here's the non-working approach:

inp3 = Input(shape = (None, 1))
x3 = inp3
x3 = Dense(2)(x3)
model3 = Model(inputs = inp3, outputs = x3)
model3.compile(optimizer = Adam(), loss='mean_squared_error')
model3.summary()
brk_train = brk.reshape(1, 100, 1)
y_train = np.transpose(y).reshape(2,100,1)
history = model3.fit(brk_train, y_train,
                batch_size=1,
                epochs=1000, verbose = 1)

ValueError: Error when checking target: expected dense_40 to have shape (None, 2) but got array with shape (100, 1)

I am trying to fit the same model as model1, but with a different shape, so that it is compatible with the TCN model -- and importantly so that it will have the same batching structure.

The output should ultimately have the shape (2, 100, 1) in this example. Basically I want the model to do the following algorithm:

• ingest X of shape (N, time_steps, dims)
• Lose the first dimension, because the design matrix is going to be identical for every series, yielding X1 of shape (time_steps, dims)
• Forward step: np.dot(X1, W), where W is of dimension (dims, N), yielding X2 of dimension (time_steps, N)
• Reshape X2 to (N, time_steps, 1). Then I can add it to the output of the other part of the model.
• Backwards step: since this is just a linear model, the gradient of W with respect to the output is just X1

How can I implement this? Do I need a custom layer?

I'm building off of ideas in this paper, in case you're curious about the motivation behind all of this.

EDIT: After posting, I noticed that I used only the time variable, rather than the time series itself. A TCN fit with the lagged series fits the idiosyncratic part of the series just fine (in-sample anyway). But my basic question still stands -- I want to merge the two types of networks.