Course Outline

ARIMA Models for Time Series Forecasting

Advanced Forecasting Techniques

Evaluating and Fine-tuning Forecast Models

Exploratory Data Analysis for Time Series

Introduction to Prophet for Time Series Forecasting

Introduction to Time Series Analysis

Real-world Applications of Time Series Analysis

Summary and Next Steps

  • Case studies of time series forecasting
  • Practical exercises with real-world datasets
  • Next steps for time series analysis in Python
  • Handling missing data in time series
  • Multivariate time series forecasting
  • Customizing forecasts with external regressors
  • Overview of Prophet for time series forecasting
  • Implementing Prophet models in Google Colab
  • Handling holidays and special events in forecasting
  • Overview of time series data
  • Components of time series: trend, seasonality, noise
  • Setting up Google Colab for time series analysis
  • Performance metrics for time series forecasting
  • Fine-tuning ARIMA and Prophet models
  • Cross-validation and backtesting
  • Understanding ARIMA (AutoRegressive Integrated Moving Average)
  • Choosing parameters for ARIMA models
  • Implementing ARIMA models in Python
  • Visualizing time series data
  • Decomposing time series components
  • Detecting seasonality and trends

Requirements

Audience

  • Data analysts
  • Data scientists
  • Professionals working with time series data
  • Intermediate knowledge of Python programming
  • Familiarity with basic statistics and data analysis techniques
 21 Hours

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