Neural Architecture Search (NAS) for Financial Time-Series Forecasting

In the financial markets, accurate forecasting is crucial for making informed investment decisions. Traditional time-series forecasting methods often require manual feature engineering and model selection, which can be time-consuming and error-prone. Neural Architecture Search (NAS) offers a promising solution by automating the process of designing optimal prediction models.

In this tutorial, we will explore how NAS can be applied to financial time-series forecasting using real-world data. We will leverage the power of automated machine learning to search for the best neural network architecture for predicting stock prices. To make our tutorial more engaging, we will focus on forecasting the prices of diverse assets such as Tesla (TSLA), Bitcoin (BTC-USD) and Gold (GC=F) until the end of February 2024.

Photo by Sean Pollock on Unsplash

Importing Necessary Libraries

Before we begin, let’s import the required libraries for our project. We will use yfinance to download financial data, numpy for numerical operations, keras for building neural networks and matplotlib.pyplot for plotting.

import yfinance as yf
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt