Overview | Key Capabilities | Getting Started | Installation | Basic Workflow | 1. Import Your Glint Export | 2. Get a Question-Level Summary | 3. Focus on Specific Questions | 4. Explore Response Distributions | Multi-Cycle Trend Analysis | Manager-Level Analysis | Roll Up to Manager Level | Demographic Analysis | Segment by Employee Attributes | Pre-Joining Attributes for Multiple Analyses | Attrition Risk Analysis | Segment Attrition by Demographics | Correlation and Factor Analysis | Understand Which Items Move Together | Factor Analysis | Working with Comments | Full-Text Comment Search | Convert to Long Format for NLP | Separating Quantitative and Qualitative Data | Privacy and Data Handling | Minimum Group Sizes | Local Processing | Additional Resources

Why an AI agent in finnts? | What the agent produces | Prerequisites | End-to-end: first run with the AI agent | 1) Create a project | 2) Bring data | 3) Define the LLMs | 4) Create the agent run | 5) Let the agent iterate to a best run | 6) Retrieve results | Ask questions about your forecast results | How it works | Example questions | What data sources are available | Tips for effective questions | Updating with new data (production loop) | Hierarchies (optional) | External regressors (xregs) | Parallelism knobs | Reading artifacts directly (optional)

Chronos2 | Authentication and Setup | Using Chronos2 within finnts | Data Size Requirements and Automatic Padding | Global Model Support | Chronos Bolt Base | Using Chronos Bolt Base within finnts | Chronos Bolt Tiny | Using Chronos Bolt Tiny within finnts | TimesFM | Using TimesFM within finnts | TimeGPT | Azure-hosted TimeGPT | Option A: Environment variables | Option B: Using nixtlar helper | Nixtla API | Option A: Environment variable | Quick start with nixtlar (standalone) | Using TimeGPT within finnts | Long-Horizon Forecasting | Hyperparameter Tuning

Univariate vs Multivariate Models | Global vs Local Models | Ensemble Models | Multistep Horizon Models | Leveraging the Tidymodels Framework

1. Bring Data | 2. Set Up Agent | 3. Iterate Forecast | 4. Analyze Forecast Output | 5. Update Forecast

Missing Data and Outliers | Understanding "_original" Columns | Box-Cox | Differencing | Date Features | Lags, Rolling Windows, and Polynomial Transformations | Custom Approaches | Model Specific Preprocessing

Get Data and Set Run Info | Prep the Data | Train Individual Models | Train Ensemble Models | Final Models | Get Forecast Results

Local Machine | Within Azure using Spark

Introduction | The dataset | Training the model | Using the lightgbm() function | Using the lgb.train() function | References

Historical Values Only | Historical and Future Values

Standard Hierarchy | Grouped Hierarchy | External Regressors | Spark Parallel Processing | Corner Cases in Forecast Reconciliation

Feature Selection Techniques | Target Correlation | Leave One Feature Out (lofo) | Boruta | Model Variable Importance | Voting Process

Manage Dependencies | Azure ML Pipelines