AI for Time Series Analysis and Forecasting
Build, evaluate, and deploy AI-powered time-series forecasting models using ARIMA and TensorFlow / Keras.
Get Course Info
Audience: Software Architects · Developers
Duration: 3 days
Format: Lectures & demos/labs (50 % / 50 %)
Overview
Enterprise software increasingly relies on smart forecasting. This course equips architects and engineers with classic methods (ARIMA, Autocorrelation) and modern deep-learning approaches (RNN, LSTM in TensorFlow) for time-series prediction.
Objective
Build, evaluate, and deploy AI-powered time-series forecasting models using ARIMA and TensorFlow / Keras.
What You Will Learn
- Time-series theory & autocorrelation
- ARIMA models & Dickey–Fuller test
- Neural-network architectures for forecasting (RNN, LSTM)
- TensorFlow implementation & GPU usage
- Validation & metrics for time-series models
Course Details
Audience: Software Architects · Developers
Duration: 3 days
Format: Lectures & demos/labs (50 % / 50 %)
- Programming language familiarity
- Linux CLI navigation
Setup: Zero-install cloud lab · SSH client · Browser
Detailed Outline
- History & types of AI
- Training ML models
- Prediction demos
- ARIMA forecasting
- Autocorrelation
- Dickey–Fuller test
- TensorFlow intro
- Perceptron, CNN, LSTM
- Forecasting with RNN/LSTM
- Validation & metrics
Ready to Get Started?
Contact us to learn more about this course and schedule your training.