Description

Introduction

Time-series forecasting plays a critical role in financial markets, IoT analytics, energy demand prediction, telecommunications, and real-time risk management. With its high-performance, columnar, in-memory architecture, kdb+ combined with the expressive power of q provides an ideal platform for large-scale time-series analysis and forecasting.

This advanced training program focuses on building robust, scalable, and high-performance forecasting solutions directly within kdb+. Participants will learn how to design forecasting pipelines, implement statistical and machine learning models using q, and optimize performance for real-time and historical datasets.

The course blends theoretical foundations of forecasting with practical implementation using kdb+, enabling participants to build production-ready predictive systems.

Prerequisites

1. Strong understanding of kdb+ fundamentals
2. Working knowledge of q programming
3. Experience with querying time-series data in kdb+
4. Basic understanding of statistics (mean, variance, regression, correlation)
5. Familiarity with financial or time-series datasets
6. Basic exposure to machine learning concepts

Table of Contents

Module 1: Foundations of Time-Series Forecasting

1. Characteristics of time-series data
2. Stationarity and seasonality
3. Trend decomposition
4. Autocorrelation (ACF) and Partial Autocorrelation (PACF)
5. Forecasting use cases in finance and real-time systems

Module 2: Time-Series Data Preparation in kdb+

1. Structuring historical and real-time data
2. Handling missing values and outliers
3. Time alignment and resampling
4. Windowed operations using q
5. Feature engineering for forecasting
6. Lag features and rolling statistics

Module 3: Statistical Forecasting Models in q

1. Moving averages (SMA, EMA, WMA)
2. AR, MA, ARMA, ARIMA concepts
3. Implementing AR models in q
4. Rolling regression techniques
5. Volatility modeling basics
6. Error metrics (MAE, RMSE, MAPE)

Module 4: Advanced Forecasting Techniques

1. Seasonal models and decomposition
2. Multivariate time-series forecasting
3. Vector autoregression (VAR) concepts
4. Regime detection basics
5. State-space modeling concepts

Module 5: Machine Learning for Time-Series in kdb+

1. Feature matrix construction in q
2. Supervised learning setup for forecasting
3. Linear regression and regularization
4. Decision-tree based forecasting logic
5. Integrating Python ML models via IPC
6. Model evaluation and cross-validation

Module 6: Real-Time Forecasting Pipelines

1. Streaming data ingestion
2. Incremental model updates
3. Rolling forecast windows
4. Low-latency prediction pipelines
5. Deploying forecasting models in production

Module 7: Performance Optimization

1. Memory optimization strategies
2. Vectorized computations in q
3. Partitioning strategies for large datasets
4. Parallel processing techniques
5. Benchmarking forecasting workloads

Module 8: Risk, Backtesting & Validation

1. Walk-forward validation
2. Backtesting strategies
3. Forecast bias detection
4. Confidence intervals and uncertainty estimation
5. Model monitoring in production

Module 9: Enterprise Implementation & Case Study

1. End-to-end forecasting architecture in kdb+
2. Financial market case study (tick or OHLC data)
3. Energy demand or IoT forecasting use case
4. Deployment best practices
5. Governance and auditability