Master Time Series Analysis and Forecasting with Python 2025

Master Time Series Analysis and Forecasting with Python 2025
2025-03-05
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

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

What you’ll learn
Apply Exponential Smoothing and Holt-Winters methods to seasonal and trend-based time series data to create accurate 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.
Requirements
Basic Statistics: Linear regression, p-value

Description
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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