Master Time Series Analysis and Forecasting with Python 2025

Time Series with Deep Learning (LSTM, TFT, N-BEATS), GenAI (Amazon Chronos), Prophet, Silverkite, ARIMA. Demand Forecast

Created by: Diogo Alves de Resende
Analytics and Data Science expert

What Will I 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.

Requirements

• Basic Statistics: Linear regression, p-value

Target audience

• 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.