# DeepSeek-TS

**Repository Path**: gracewu001/DeepSeek-TS

## Basic Information

- **Project Name**: DeepSeek-TS
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-02-22
- **Last Updated**: 2025-02-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# DeepSeek-TS: A State-Space Enhanced Framework for Multi-Product Time Series Forecasting
Integrating Extended Multi-Head Latent Attention with Mamba-Style State Dynamics and Group Relative Policy Optimization for Robust Sales Prediction
I was impressed by DeepSeek's tech - its efficient Multi-Head Latent Attention (MLA) and Group Relative Policy Optimization (GRPO) techniques got me thinking about how to apply them to multi-product time series forecasting. In our approach, we extend MLA into what we call MLA-Mamba, making it evolve dynamically over time for forecasting tasks, while GRPO fine-tunes our predictions based on real-time feedback.
Simply put, MLA compresses and extracts key features from sales data. We take it a step further by letting these features evolve through a state-space model with non-linear activations - kind of like giving our model a memory that adapts to trends, just like a sales team adjusts strategies during market surges.
Alongside this, GRPO adds a smart decision-making process that continually evaluates and refines predictions against a baseline, much like a manager tweaking forecasts on the fly. This dynamic method helps our model keep up with sudden changes in sales patterns.
We compare our method with classical ARMA models and modern GRU-based networks. While ARMA works well for linear trends and GRUs capture temporal dependencies, our DeepSeek-TS framework shines by modeling complex inter-product relationships and adapting to non-linear dynamics, leading to more accurate and robust forecasts.
In the following sections, we break down the technical details of our extended MLA (MLA-Mamba) and GRPO frameworks, and show how they work together to enhance multi-product time series forecasting.