Master Time Series Analysis and Forecasting with Python 2024

Master Time Series Analysis and Forecasting with Python 2024, available at $84.99, has an average rating of 4.42, with 394 lectures, 32 quizzes, based on 702 reviews, and has 5394 subscribers.

You will learn about Understand the fundamental principles of time series data and its significance in forecasting across various industries. Differentiate between various time series forecasting models such as Exponential Smoothing, ARIMA, and Prophet, identifying when to use each model. Apply Exponential Smoothing and Holt-Winters methods to seasonal and trend-based time series data to create accurate forecasts. Implement SARIMA and SARIMAX models in Python, incorporating external variables to enhance the predictive power of your forecasts. Develop time series models using advanced techniques such as Temporal Fusion Transformers (TFT) and N-BEATS to handle complex datasets. Optimize forecasting models by tuning parameters and using ensemble methods to improve accuracy and reliability. Evaluate the performance of different forecasting models using metrics such as MAE, RMSE, and MAPE, ensuring the robustness of your predictions. Code Python scripts to automate the entire time series forecasting process, from data preprocessing to model deployment. Implement deep learning models such as RNN and LSTM to accurately forecast complex time series data, capturing long-term dependencies. Develop and optimize advanced forecasting solutions using Generative AI techniques like Amazon Chronos, incorporating state-of-the-art methods. This course is ideal for individuals who are Business analysts looking to improve their forecasting skills and techniques. or Data scientists interested in applying time series analysis and forecasting to business problems. or Marketing professionals looking to forecast future demand for products or services. or Financial analysts seeking to forecast future trends and performance for businesses. or Operations managers looking to improve demand planning and forecasting for their organization. It is particularly useful for Business analysts looking to improve their forecasting skills and techniques. or Data scientists interested in applying time series analysis and forecasting to business problems. or Marketing professionals looking to forecast future demand for products or services. or Financial analysts seeking to forecast future trends and performance for businesses. or Operations managers looking to improve demand planning and forecasting for their organization.

Enroll now: Master Time Series Analysis and Forecasting with Python 2024

Summary

Title: Master Time Series Analysis and Forecasting with Python 2024

Price: $84.99

Average Rating: 4.42

Number of Lectures: 394

Number of Quizzes: 32

Number of Published Lectures: 389

Number of Curriculum Items: 426

Number of Published Curriculum Objects: 389

Original Price: €219.99

Quality Status: approved

Status: Live

What You Will Learn

• Understand the fundamental principles of time series data and its significance in forecasting across various industries.
• Differentiate between various time series forecasting models such as Exponential Smoothing, ARIMA, and Prophet, identifying when to use each model.
• Apply Exponential Smoothing and Holt-Winters methods to seasonal and trend-based time series data to create accurate forecasts.
• Implement SARIMA and SARIMAX models in Python, incorporating external variables to enhance the predictive power of your forecasts.
• Develop time series models using advanced techniques such as Temporal Fusion Transformers (TFT) and N-BEATS to handle complex datasets.
• Optimize forecasting models by tuning parameters and using ensemble methods to improve accuracy and reliability.
• Evaluate the performance of different forecasting models using metrics such as MAE, RMSE, and MAPE, ensuring the robustness of your predictions.
• Code Python scripts to automate the entire time series forecasting process, from data preprocessing to model deployment.
• Implement deep learning models such as RNN and LSTM to accurately forecast complex time series data, capturing long-term dependencies.
• Develop and optimize advanced forecasting solutions using Generative AI techniques like Amazon Chronos, incorporating state-of-the-art methods.

Who Should Attend

• 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.

Target Audiences

• 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.