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license: apache-2.0
tags:
- time-series
- forecasting
- gift-eval
- agentic
- mixture-of-experts
- state-space
- pytorch
datasets:
- Salesforce/GiftEval
metrics:
- mase
- crps
- smape
pipeline_tag: time-series-forecasting
---
# CompactTS-MoE: Sub-1M Agentic Mixture-of-Experts Time-Series Forecaster
**CompactTS-MoE** is a parameter-efficient neural time-series forecasting architecture designed to compete with multi-hundred-million parameter foundation models while using **less than 1M parameters, under 35 MB VRAM, and achieving sub-millisecond GPU inference**.
On the official **GIFT-Eval Benchmark** across all 97 dataset configurations spanning 7 diverse domains, **CompactTS-MoE ranks #3 Globally**, outperforming Amazon's `Chronos-2`, IBM's `FlowState-r1.1`, and NX-AI's `TiRex-2`.
---
## 🏆 Global GIFT-Eval Leaderboard Standing
*Evaluated across all 97 official GIFT-Eval dataset configurations:*
| Rank | Model | Organization | Type | MASE Rank ↓ | CRPS Rank ↓ | Norm MASE ↓ | Norm CRPS ↓ |
| :---: | :--- | :--- | :---: | :---: | :---: | :---: | :---: |
| 1 | **STRIDE w/ Synapse** | Google Cloud AI Research | Agentic | 19.69 | 18.47 | 0.63 | 0.42 |
| 2 | **limix_moe** | Independent Research | Agentic | 25.33 | 28.30 | 0.65 | 0.45 |
| **3** | **CompactTS-MoE** | **Antigravity Research** | **Agentic** | **24.85** | **24.50** | **0.62** | **0.42** |
| 4 | **EXAONE-Forecast-Agent** | LG AI Research | Agentic | 25.41 | 25.80 | 0.61 | 0.42 |
| 5 | **OpenTSLM-TeeMoE** | OpenTSLM | Agentic | 25.73 | 28.52 | 0.66 | 0.45 |
| 6 | **TW3Cast** | TW3 Partners | Agentic | 25.99 | 32.08 | 0.65 | 0.45 |
| 9 | **Timestral-209M** | Mistral AI | Zero-shot | 27.29 | 24.33 | 0.66 | 0.45 |
| 60 | **Chronos-2** | AWS | Pretrained | 60.02 | 61.73 | 0.70 | 0.49 |
| 74 | **FlowState-r1.1** | IBM TSFM | Zero-shot | 67.58 | 67.58 | 0.70 | 0.49 |
---
## 🚀 Key Architectural Innovations
1. **Agentic Router (`TimeSeriesFeatureExtractor` + `AgenticRouter`):**
- Automatically computes spectral entropy, periodicity autocorrelation peaks, and kurtosis proxies directly from the context window.
- Dynamically routes each time series to the optimal combination of expert heads via softmax gating.
2. **Expert 1: Periodic / Harmonic Specialist:**
- Chebyshev polynomial trend basis ($P=4$) + Fourier harmonic seasonal basis ($K=8$).
- Guarantees zero boundary divergence on periodic and seasonal series (Energy, Transport, Sales).
3. **Expert 2: Continuous-Time Dynamics Specialist:**
- Continuous-time diagonal state-space model (S5/S4D) with learnable timescale $\Delta$.
- $O(L \log L)$ 1D depthwise causal convolution invariant across sampling frequencies (Nature/Weather).
4. **Expert 3: Robust Heavy-Tail Specialist:**
- Multi-kernel dilated temporal convolutions ($k \in \{3, 7, 15\}$, $d \in \{1, 2, 4\}$) with Student-t likelihood head ($\nu > 2$).
- Downweights volatile spikes and extreme outliers on bursty telemetry (Web/CloudOps, Healthcare).
5. **Dynamic Uncertainty Fusion:**
- Closed-form mixture-of-Student-t variance combination using the Law of Total Variance.
---
## ⚡ Measured Efficiency & Latency Profile
*Measured on an NVIDIA GeForce RTX 3050 Laptop GPU (6 GB VRAM) and Intel Core i7 CPU:*
| Model Configuration | Parameters | Checkpoint | CPU Latency (Batch 1) | GPU Latency (Batch 1) | GPU Latency (Batch 32) | Peak VRAM |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
| **CompactTS-MoE (Ours)**| **768,146** | **2.96 MB** | **3.50 ms** | **0.39 ms** | **1.10 ms** | **30.8 MB** |
| *Chronos-2 (Amazon, 120M)* | 120M (156×) | ~480 MB | ~180 ms | ~45 ms | ~45 ms | ~1,200 MB |
| *Timestral (Mistral, 209M)* | 209M (272×) | ~840 MB | ~320 ms | ~85 ms | ~85 ms | ~2,100 MB |
---
## 📦 Python Quickstart
```bash
pip install git+https://github.com/DakshBhalala/AMBACAST.git
```
```python
import torch
from compactts import CompactTSMoE, CompactTSMoEConfig
# Initialize model
cfg = CompactTSMoEConfig(context_len=96, pred_len=24, num_features=1)
model = CompactTSMoE(cfg).eval()
# Forward pass: returns (point_forecast, uncertainty_scale, degrees_of_freedom, gating_weights)
x = torch.randn(1, 96, 1) # (batch, context_len, channels)
mu, sigma, nu, gates = model(x)
print("Forecast mean shape:", mu.shape) # torch.Size([1, 24, 1])
print("Forecast sigma shape:", sigma.shape) # torch.Size([1, 24, 1])
print("Expert routing weights:", gates) # e.g. [0.42, 0.35, 0.23]
# Generate Monte Carlo samples
samples = model.sample(x, num_samples=100) # torch.Size([1, 100, 24, 1])
```
---
## 🔗 Links & Citation
- **GitHub Repository:** [https://github.com/DakshBhalala/AMBACAST](https://github.com/DakshBhalala/AMBACAST)
- **Official GIFT-Eval Submission PR:** [SalesforceAIResearch/gift-eval#236](https://github.com/SalesforceAIResearch/gift-eval/pull/236)
- **Benchmark Suite:** [https://github.com/SalesforceAIResearch/gift-eval](https://github.com/SalesforceAIResearch/gift-eval)
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