Time Series Forecasting With Python
Published 9/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.08 GB | Duration: 2h 10m
Learn how to use Python for Forecasting time series data, using ARIMA, Prophet, Statsmodels
What you'll learn
Forecast sales and revenue for a small business using python
Make accurate forecasts, by learning about forecasting metrics, and comparing multiple forecasting models and their parameters
Read time series data from excel files, manipulate the data in python, do data cleaning and deal with missing data
Use Prophet and Seasonal ARIMA models to forecast complex time series with seasonality
Understand trend and seasonality in a time series, and how to break down trend and seasonality
Requirements
Elementary python experience with basics of pandas
Description
Welcome to Time Series Forecasting with Python. This course will teach you how to effectively analyze and forecast time series data using Python, making it ideal for anyone looking to predict future trends in areas like finance, sales, and environmental science. You will start by learning the fundamentals of time series, including how to identify key features such as trend, seasonality, and noise. The course will guide you through reading and writing time series data from Excel, enabling seamless data integration. You'll also discover various visualization techniques to help you explore and understand the structure of time series data, using real-world examples such as stock price analysis.After mastering the basics, you'll dive deeper into creating and working with time series data that exhibit both trend and seasonality. You'll learn how to decompose these components to better understand and model the data. The course then introduces the Seasonal ARIMA model, a powerful tool for forecasting time series data. You will gain both an intuitive and mathematical understanding of the model, learning how to implement it in Python, generate forecasts, and visualize the results.You will also explore the Prophet model, comparing it with the Seasonal ARIMA model to understand their differences, strengths, and suitable applications. By the end of the course, you will be proficient in using these advanced forecasting techniques, evaluating the quality of your forecasts, and refining them for better accuracy. This hands-on experience with real-world datasets will equip you with the skills needed to handle complex time series forecasting challenges with confidence.
