Time Series Analysis - Basics

Time series analysis is a statistical technique used to analyze data that is collected over time. It can be used to identify trends, patterns, and relationships in the data, as well as to make predictions about future values. Time series analysis is used in a wide variety of fields, including finance, economics, engineering, and science.

Some examples of time series data include:

• Stock prices
• Economic indicators such as GDP and unemployment rates
• Sales data
• Weather data
• Sensor data

Time series analysis can be used to answer a variety of questions about time series data, such as:

• What is the overall trend in the data?
• Are there any seasonal patterns in the data?
• What are the relationships between different time series?
• How can we predict future values of the time series?

Here are some examples of how time series analysis is used in the real world:

• Financial analysts use time series analysis to predict future stock prices and other financial market movements.
• Economists use time series analysis to forecast economic growth, unemployment, and other economic indicators.
• Retail companies use time series analysis to predict customer demand and inventory levels.
• Weather forecasters use time series analysis to predict future weather conditions.
• Engineers use time series analysis to monitor and control industrial processes.

Time series analysis is a powerful tool that can be used to extract valuable insights from time series data. By understanding the trends, patterns, and relationships in the data, we can make better decisions and predictions about the future.

?Let's delve into the practical aspects of Time Series Analysis for Financial Markets, covering various topics using Python code. In this article, we will explore the following key areas:

1.??Installing necessary libraries

2.??Extracting End-of-Day Data for a Single Security - Retrieve daily closing prices for a specific security using Python.?

3.??Extracting End-of-Day Data for Multiple Securities - Extend the example to fetch end-of-day data for multiple securities.

4.??Extracting Intraday Data - Show how to retrieve intraday data for a specific security.

5.??Resampling - Illustrate how to resample data to a different frequency (e.g., weekly or monthly).

6.??Visualization through Interactive Charts - Demonstrate various interactive chart visualizations using Python libraries like Cufflinks.

This link delves into the exploration of data from Nifty, Reliance, Bharti Airtel, TCS, HUL, and HDFC Bank. The analysis involves the creation of diverse charts, such as Price series using line charts, OHLC, Candlestick, subplots, normalized plots, rolling returns, and normal distribution charts.