In this article, I’ll guide you through the process of building time series models using TensorFlow, a powerful framework for constructing and training neural networks. I’ll show you a variety of neural network architectures for time series forecasting, ranging from simple models like SimpleRNN to more complex ones such as LSTM. Additionally, I’ll present advanced visualization techniques to I’ve used to make and visualize predictions beyond the validation period.
I’ve used the following libraries: TensorFlow with Keras for building neural networks, Matplotlib for visualization, NumPy for numerical operations, and Scikit-Learn for data preprocessing.
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
from sklearn.preprocessing import MinMaxScaler
Data preparation is fundamental for the success of any machine learning model. In this section, I will perform several steps to prepare the data for training and validation.
Separating Data and Time Steps
The first step is to separate the time steps from the actual data.
For Short Time Series Data (data stored in an array): we can create an array of time steps using ‘np.arange()’:
#For short time series data, data stored in an array, I'll do the following:
dummy_data = np.array([1, 2, 3,...])
time_step = np.arange(len(dummy_data))
For Larger Datasets Stored in Files (e.g., CSV Files): we can read the data and corresponding time steps from the file:
#For larger datasets stored in files, such as CSV files
import csvtime_step = []
data = []
with open("file.txt", "r", encoding="utf-8") as f:
csv_reader = csv.reader(f, delimiter=",")
# Skip the header
next(csv_reader)
# Skip lines with NUL characters
lines = (line for line in csv_reader if "\0" not in line)
# Iterate…