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
