keras how to feed an input variable with the output of my model

Question

Im turning around since a years with this problem, I want to forcast t+1 using the forcast t+0 as one of my input. All I find is running my model one step at time and manualy insert my last forcast in the input for the next one step run... not efficient and impossible to train.

I use keras with tensorflow. Thank for any help!

Answer 1

I suggest u ChainRegressor/Classifier from sklearn. as u specify this model iterate fit in each step using the previous predictions as features for the new fit. here an example in a regression task

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import *
from sklearn.multioutput import RegressorChain

n_sample = 1000
input_size = 20

X = np.random.uniform(0,1, (n_sample,input_size))
y = np.random.uniform(0,1, (n_sample,3)) <=== 3 step forecast

def create_model():
    
    global input_size
    
    model = Sequential([
        Dense(32, activation='relu', input_shape=(input_size,)),
        Dense(1)
    ])

    model.compile(optimizer='Adam', loss='mse')
    input_size += 1 # <== important 
    # increase the input dimension and include the previous predictions in each iteration
    
    return model

model = tf.keras.wrappers.scikit_learn.KerasRegressor(build_fn=create_model, epochs=1, 
                                                       batch_size=256, verbose = 1)
chain = RegressorChain(model, order='random', random_state=42)
chain.fit(X, y)

chain.predict(X).shape