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!
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 
