Description

In this tutorial, you will learn the fundamental techniques for working with time series data, which is data collected over time, such as stock prices, weather forecasts, or sensor readings. Time series data has unique characteristics, such as seasonality, trends, and autocorrelation, which need to be understood and handled appropriately for effective analysis.

You will begin by learning how to load and inspect time series data. The tutorial will guide you through the process of converting raw data into a time series format, ensuring that date and time values are properly parsed and indexed. You will also understand the importance of setting a correct datetime index and how it impacts the analysis and modeling process.

Next, you’ll focus on resampling and frequency conversion — essential tasks in time series analysis. You’ll learn how to handle different time granularities (e.g., daily, weekly, monthly) by resampling the data using various techniques like upsampling (increasing the frequency) and downsampling (reducing the frequency). This is critical when dealing with data collected at irregular intervals or when you need to aggregate data over specific time periods.

A significant part of time series analysis is handling missing data. In this tutorial, you’ll explore strategies for dealing with missing values, including forward/backward filling, interpolation, and dropping missing values, depending on the nature of the data and the context.

You will also learn to identify and model seasonality and trends. Seasonality refers to regular patterns that repeat over time (e.g., monthly sales peaks), while trends represent long-term increases or decreases in data. Techniques like moving averages and decomposition (breaking data into trend, seasonality, and residual components) will help you understand and visualize these patterns.

The tutorial will cover stationarity — a key concept in time series modeling — and show you how to check whether your data is stationary. You will learn how to apply transformations, such as differencing or logarithmic scaling, to achieve stationarity if necessary.

Finally, you’ll practice these techniques using a real-world time series dataset (e.g., stock prices, temperature readings, or sales data). You’ll clean the data, handle missing values, resample it, and visualize the seasonal and trend components.

By the end of this tutorial, you’ll be able to effectively process and analyze time series data, prepare it for forecasting, and understand key patterns like seasonality and trends.

You’ll also receive a Jupyter Notebook with code examples, a PDF summary of time series processing techniques, and practice exercises to reinforce your learning.