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README.md

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+---
+language:
+- en
+license: apache-2.0
+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:
+
+1. **Series Profiler:** Extracts structural statistical and spectral characteristics from incoming series (Fourier spectral density, dominant seasonal lag, trend variance ratio, and kurtosis).
+2. **Meta-Routing Policy:** Maps the time series to domain-specialized open-weights foundation model representations (`TimesFM-3`, `Chronos-2`, `Toto-2.0`).
+3. **Calibrated Quantile Fusion:** Enforces strict quantile monotonicity ($q_{0.1} \le q_{0.2} \le \dots \le q_{0.9}$) and harmonic basis alignment.
+
+All underlying foundation models utilized in this agentic framework are public open-source weights licensed under Apache-2.0.
+
+## Installation & Quickstart
+
+```bash
+git clone https://github.com/DakshBhalala/AMBACAST.git
+cd AMBACAST
+pip install -e .
+```
+
+### Python API Usage
+
+```python
+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 is evaluated against the 97 dataset configurations of the **Salesforce GIFT-Eval** benchmark under `model_type: "agentic"`.
+
+To reproduce results:
+```bash
+python scripts/run_agentic_benchmark.py
+```
+
+## Citation & Authorship
+
+- **Author:** Daksh Bhalala
+- **Code Repository:** [DakshBhalala/AMBACAST](https://github.com/DakshBhalala/AMBACAST)
+- **License:** Apache-2.0