Zero-Inflated Time Series Generation: Neural Networks Addressing the Challenges of Sparse Data

This post introduces the open-source project Zero-Inflated-Time-Series-Generation published by ArdeleanRichard on GitHub. The project focuses on the generation of zero-inflated time series (sparse/intermittent time series), exploring the characteristics, application scenarios, and deep learning solutions for this type of data. Zero-inflated time series pose challenges to traditional models due to their high proportion of zero values and random non-zero values. The project generates synthetic data via neural networks, which has multiple values such as data augmentation and privacy protection.

A zero-inflated time series refers to a sequence where most values are zero, with non-zero values appearing only at a few time points—also known as sparse or intermittent time series. A typical example is sales records of high-end products (selling one item every few days/weeks). Application scenarios include retail supply chains (long-tail product demand), equipment maintenance (fault records), healthcare (rare disease incidence), network traffic (event peaks), and finance (changes in low-liquidity assets). Modeling difficulties include extreme imbalance (zero values account for over 95%), randomness of non-zero values, difficulty in estimating the size of non-zero values, and irregular time intervals—traditional models (such as ARIMA) perform poorly.

The value of generating zero-inflated time series includes: 1. Data augmentation: Expand training sets when real data is scarce; 2. Privacy protection: Replace real data in sensitive fields (healthcare/finance); 3. Stress testing: Generate extreme scenarios to test models; 4. Algorithm validation: Verify new algorithms under controlled conditions; 5. Simulation: Simulate strategy effects in scenarios like supply chain optimization.

The technical solutions are divided into two-stage models and end-to-end neural networks:

• Two-stage model: The first stage uses classification models (logistic regression, neural networks, etc.) to predict whether a value is zero; the second stage uses regression models (log-normal distribution, etc.) to predict non-zero values.
• End-to-end solutions: Include Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), autoregressive models (Transformer/WaveNet), and diffusion models. Evaluation metrics cover dimensions such as zero-value ratio, non-zero value distribution, time patterns, sequence correlation, and diversity.

In-depth analysis of application scenarios:

• Supply chain and inventory management: Optimize long-tail product inventory, replenishment strategies, and risk assessment;
• Healthcare: Rare disease monitoring (privacy-protected data sharing), drug adverse reaction modeling, and epidemic simulation;
• Financial risk management: Credit default stress testing, high-frequency trading strategy validation, and insurance claim simulation.

Current challenges: Difficulty in pattern learning (sparsity leads to overfitting), difficulty in evaluation (traditional metrics are not applicable), and difficulty in conditional generation (controlling attributes like zero-value ratio). Cutting-edge directions: Deep probabilistic models (deep Gaussian processes, neural ODEs), reinforcement learning-based generation, causal inference, and multimodal fusion (combining text/images).

Zero-inflated time series are an important challenge in time series analysis with wide applications (retail, healthcare, finance, etc.). This project uses neural networks to generate such data, which has both theoretical and practical value. With the development of generative AI, related technologies will continue to mature, providing accurate prediction and decision support for industries. For data scientists, this is an exploration field with clear problems and significant value.