Introduction, Data Structure & Preprocessing, Exploratory Analysis, Time Series Decomposition, AR/MA/ARMA/ARIMA Models,

What you'll learn

Grasp the fundamental structure and distinct characteristics of time series data.
Learn the differences between time series and cross-sectional data.
Carry out preprocessing and data wrangling for datasets that are time-based.
Analyze time series data to identify patterns, including trends, seasonality, and cyclical behavior.

Requirements

This course has no strict prerequisites, making it suitable for beginners. A basic understanding of statistics, linear algebra, and Python is helpful. Familiarity with Pandas and plotting libraries like Matplotlib or Seaborn can enhance your experience but isn't required. The key requirement is a willingness to learn and explore real-world time series applications.

Description

Time Series Analysis (TSA) is an important area of study used in many fields, including industries, government, and education. It helps with various tasks such as planning inventory and demand, creating marketing strategies, deciding how to allocate capital, setting prices, maintaining machinery, and forecasting the economy. With the rise of time-stamped data, like weekly unemployment claims, minute-by-minute stock prices, daily sales, sensor data, and wearable device logs, forecasting relies more on data than ever.This course offers a clear introduction to time series analysis and forecasting. You will learn about the special features of time series data compared to other types of data and how to handle and analyze time series datasets. The course includes basic statistical measures, visualisations, and key statistical models used in time series analysis.You will start with the basics, including how time series data is structured, how to clean and prepare data, and how to create useful features from it. As the course continues, you will gain practical experience with common time series models, such as Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), and Autoregressive Integrated Moving Average (ARIMA) models. By the end of the course, you will know how to analyse real-time series data and create accurate forecasting models.

Who this course is for

This course is ideal for students, analysts, data scientists, and professionals seeking to learn time series analysis and forecasting. It's valuable for those in planning or decision-making roles, with no prior experience required, making it suitable for beginners and those looking to enhance their skills.
Homepage:

https://www.udemy.com/course/time-series-analysis-l/

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