Forecasting Models and Time Series for Business in Python

• Use time series analysis to identify patterns and trends in time series data in Python.
• Select appropriate forecasting models for different types of time series data in Python.
• Develop demand planning and forecasting models using time series analysis techniques in Python.
• Implement Holt-Winters exponential smoothing in Python for time series forecasting.
• Implement SARIMAX models for time series forecasting in Python.
• Utilize Facebook Prophet for forecasting future values of time series data.
• Apply Tensorflow Structural Time Series to forecast time series data using machine learning techniques.
• Implement XGBoost for time series forecasting in Python.
• Understand the assumptions and limitations of different time series forecasting models in Python.
• Evaluate the performance of different time series forecasting models in Python.

Who this course is for:

• Business analysts looking to improve their forecasting skills and techniques.
• Data scientists interested in applying time series analysis and forecasting to business problems.
• Marketing professionals looking to forecast future demand for products or services.
• Financial analysts seeking to forecast future trends and performance for businesses.
• Operations managers looking to improve demand planning and forecasting for their organization.

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