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Forecasting Economic Indicators using Time Series Analysis
Economic indicators play a crucial role in understanding the overall health and performance of an economy. Time series analysis provides a powerful framework for forecasting economic indicators, enabling businesses and policymakers to make informed decisions. In this tutorial, we will explore the process of forecasting economic indicators using Python. We will leverage time series analysis techniques, including data visualization, statistical modeling and machine learning, to predict future trends in economic data.
Getting Started with Time Series Analysis
Before diving into the forecasting process, let’s first understand the basics of time series analysis. Time series data consists of observations recorded at regular time intervals, making it essential for analyzing trends and patterns over time. In this tutorial, we will focus on financial time series data, specifically stock prices, as they are widely used as economic indicators.
Installing Required Libraries
To begin, we need to install the necessary Python libraries for data retrieval, analysis and visualization. We will use the yfinance library to download historical stock price data and pandas, numpy and matplotlib for data manipulation and visualization.
