Do you want to try some other methods to solve your forecasting problem rather than traditional regression? There are many neural network architectures, which are frequently applied in NLP field, can be used for time series as well. In this article, we are going to build two Seq2Seq Models in Keras, the simple Seq2Seq LSTM Model, and the Seq2Seq LSTM Model with Luong Attention, and compare their forecasting accuracy.

import random
import numpy as np
import matplotlib.pyplot as plt

import pickle as pkl
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import LSTM, Dense, RepeatVector, TimeDistributed, Input, BatchNormalization, \
    multiply, concatenate, Flatten, Activation, dot
from keras.optimizers import Adam
from keras.utils import plot_model
from keras.callbacks import EarlyStopping
import pydot as pyd
from keras.utils.vis_utils import plot_model, model_to_dot
keras.utils.vis_utils.pydot = pyd

#attention #time-series-forecasting #seq2seq #keras #lstm