Description

Introduction

Time series forecasting plays a key role in industries like finance, retail, and manufacturing. Vertex AI enables you to build, train, and deploy scalable time series forecasting models using AutoML Forecasting or custom pipelines with deep learning frameworks like TensorFlow. This module guides you through end-to-end forecasting workflows in Vertex AI.

Prerequisites

• Understanding of basic machine learning concepts

• Familiarity with time series data and patterns (trend, seasonality)

• A time-stamped dataset in CSV or BigQuery

• Google Cloud project with Vertex AI and AutoML enabled

• IAM roles: Vertex AI Admin, BigQuery Admin (if applicable)

Table of Contents

1. Introduction to Time Series Forecasting
   1.1 What is Time Series Forecasting?
   1.2 Use Cases Across Industries
   1.3 Forecasting Challenges and Opportunities
   1.4 Key Features of AutoML Forecasting in Vertex AI

2. Preparing Time Series Data
   2.1 Dataset Structure and Requirements
   2.2 Time Granularity and Timestamp Formatting
   2.3 Including Contextual (Static/Covariate) Features
   2.4 Uploading Data to BigQuery or Cloud Storage

3. Creating Datasets for Forecasting
   3.1 Using BigQuery as a Data Source
   3.2 Schema and Label Column Guidelines
   3.3 Validating Input Data and Detecting Anomalies
   3.4 Time Series Granularity and Forecast Horizon

4. Building Time Series Models with AutoML
   4.1 Configuring AutoML Forecasting Tasks
   4.2 Forecasting Horizon and Context Window
   4.3 Model Training and Evaluation Metrics (RMSE, MAE)
   4.4 Cross-validation and Backtesting

5. Using Vertex AI Workbench for Custom Models
   5.1 Forecasting with DeepAR, LSTM, or Prophet
   5.2 Feature Engineering for Time Series
   5.3 TensorFlow Time Series Pipelines
   5.4 Hyperparameter Tuning and Model Selection

6. Model Deployment and Predictions
   6.1 Registering the Forecasting Model
   6.2 Creating and Managing Endpoints
   6.3 Real-time vs Batch Forecasting
   6.4 Exporting Predictions to BigQuery or CSV

7. Monitoring and Retraining
   7.1 Setting Up Drift Detection and Alerts
   7.2 Updating Forecasts with New Data
   7.3 Scheduled Retraining Pipelines
   7.4 Model Versioning and Governance

8. Visualization and BI Integration
   8.1 Integrating Forecast Results with Looker Studio
   8.2 Visualizing Trends, Seasonality, and Forecast Uncertainty
   8.3 Dashboards for Business Stakeholders
   8.4 Real-time Forecast Updates and Reports

9. Industry Use Cases and Patterns
   9.1 Demand Forecasting in Retail
   9.2 Financial Market Predictions
   9.3 Predictive Maintenance in IoT
   9.4 Energy Consumption Forecasting

10. Best Practices and Optimization
    10.1 Choosing Forecast Horizon and Granularity
    10.2 Avoiding Data Leakage in Time Series
    10.3 Cost Optimization Tips for Long Training Runs
    10.4 Security and Access Control Best Practices

Vertex AI simplifies time series forecasting with both AutoML and custom model support.
Whether predicting sales, inventory, or sensor readings, Vertex AI delivers scalable and reliable forecasting pipelines that can integrate seamlessly into production systems.