Time Series Analysis with Google Colab

• Intermediate knowledge of Python programming
• Familiarity with basic statistics and data analysis techniques

Audience

• Data analysts
• Data scientists
• Professionals working with time series data

This course provides an introduction to time series analysis, with hands-on practice in Google Colab. Participants will explore ARIMA models, Prophet, and other time series forecasting techniques to analyze and forecast temporal data. The course emphasizes the practical implementation of these techniques within a cloud-based environment like Google Colab.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data professionals who wish to apply time series forecasting techniques to real-world data using Google Colab.

By the end of this training, participants will be able to:

• Understand the fundamentals of time series analysis.
• Use Google Colab to work with time series data.
• Apply ARIMA models to forecast data trends.
• Utilize Facebook’s Prophet library for flexible forecasting.
• Visualize time series data and forecasting results.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Time Series Analysis

• Overview of time series data
• Components of time series: trend, seasonality, noise
• Setting up Google Colab for time series analysis

Exploratory Data Analysis for Time Series

• Visualizing time series data
• Decomposing time series components
• Detecting seasonality and trends

ARIMA Models for Time Series Forecasting

• Understanding ARIMA (AutoRegressive Integrated Moving Average)
• Choosing parameters for ARIMA models
• Implementing ARIMA models in Python

Introduction to Prophet for Time Series Forecasting

• Overview of Prophet for time series forecasting
• Implementing Prophet models in Google Colab
• Handling holidays and special events in forecasting

Advanced Forecasting Techniques

• Handling missing data in time series
• Multivariate time series forecasting
• Customizing forecasts with external regressors

Evaluating and Fine-tuning Forecast Models

• Performance metrics for time series forecasting
• Fine-tuning ARIMA and Prophet models
• Cross-validation and backtesting

Real-world Applications of Time Series Analysis

• Case studies of time series forecasting
• Practical exercises with real-world datasets
• Next steps for time series analysis in Python

Summary and Next Steps