docs: update license to BSL-1.1 and repository link to CompactTS-Agent

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language:
  - en
license: other
license_name: bsl-1.1
license_link: LICENSE
tags:
  - time-series
  - time-series-forecasting
  - forecasting
  - gift-eval
  - meta-learning
  - agentic
  - pytorch
datasets:
  - Salesforce/GiftEval
pipeline_tag: time-series-forecasting

CompactTS-Agent: Autonomous Agentic Meta-Forecaster for Time Series

Developed by Daksh Bhalala.

CompactTS-Agent is an autonomous agentic meta-forecasting system designed for general time-series forecasting across diverse industrial domains (energy grids, server telemetry, traffic flow, nature observations, and healthcare).

Architecture Overview

Rather than relying on a single static neural network across disparate domains, CompactTS-Agent operates as an autonomous orchestration system:

Scale-Invariant Series Profiler: Extracts structural statistical and spectral signatures from incoming series (Fourier spectral entropy $H_s$, normalized mean drift $\Delta_\mu$, and ARCH volatility clustering $R^2_{\text{ARCH}}$).
Meta-Routing Policy: Deterministically maps time-series physical regimes to specialized foundation model representations (e.g., concentrated harmonics, low-drift telemetry, volatility clustering).
Calibrated Quantile Fusion: Enforces strict quantile monotonicity ($q_{0.1} \le q_{0.2} \le \dots \le q_{0.9}$) and physical support projections.

Installation & Quickstart

git clone https://github.com/DakshBhalala/CompactTS-Agent.git
cd CompactTS-Agent
pip install -e .

Python API Usage

import numpy as np
import pandas as pd
from compactts.agent import CompactTSAgent, SeriesProfiler

# 1. Profile an incoming time series
profiler = SeriesProfiler(freq="H")
series = np.sin(np.linspace(0, 20, 100)) + 0.1 * np.random.randn(100)
profile = profiler.profile(series)
print("Series Profile:", profile)

# 2. Generate calibrated probabilistic forecasts
agent = CompactTSAgent(prediction_length=24, freq="H")
start_dates = [pd.Period("2024-01-01 00:00", freq="h")]
forecasts = agent.forecast_batch([series], start_dates)

# Access forecast quantiles (9 levels: 0.1 to 0.9)
q_forecasts = forecasts[0].forecast_array
print("Forecast shape (9 quantiles, 24 horizon):", q_forecasts.shape)

Benchmark Evaluation

CompactTS-Agent evaluation results are submitted under model_type: "agentic".

To reproduce results on official benchmark configurations:

python scripts/run_agentic_benchmark.py

Commercial Licensing & Enterprise Deployment

CompactTS-Agent is licensed under the Business Source License 1.1 (BSL 1.1):

Free for Non-Commercial Research & Benchmark Replication: Anyone is free to inspect, run, evaluate, test, and reproduce benchmarks using CompactTS-Agent.
Commercial & Production Use: Any commercial use, deployment in production environments, hosting as a paid service/API, or integration into commercial products requires a separate commercial license agreement.

For commercial inquiries, enterprise deployment rights, or proprietary licensing:

Author: Daksh Bhalala
Code Repository: DakshBhalala/CompactTS-Agent
License: Business Source License 1.1 (BSL-1.1)