Skyloom Karachi AQI Forecasting

This is a fine‑tuned version of google/timesfm-2.5-200m-transformers for hourly Air Quality Index (AQI) forecasting in Karachi, Pakistan.
The model is optimised for a 24‑step forecast horizon (next 24 hours).

Model summary

Data

• Source: Syed110-3/karachi-aqi-predictor
• After cleaning: 26,208 hourly records (2023-01-02 to 2025-12-28)
• Split: chronological (no future leakage)
• Seasonal period for MASE: 24 hours

Evaluation results (24‑step ahead)

Note: The ensemble assigned 90% weight to the model and 0% to the zero‑shot model, confirming that fine‑tuning was essential for this dataset.

Usage

import torch
import numpy as np
from transformers import TimesFm2_5ModelForPrediction

model_id = "mahwizzzz/skyloom"  
device = "cuda" if torch.cuda.is_available() else "cpu"

model = TimesFm2_5ModelForPrediction.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16 if device == "cuda" else torch.float32,
    device_map=device
)
model.eval()

# Provide the last 1024 hourly AQI values
past_aqi = np.array([...], dtype=np.float32)

with torch.no_grad():
    batch = torch.tensor(past_aqi[-1024:][None, :], dtype=torch.float32).to(device)
    output = model(past_values=batch, forecast_context_len=1024)
    forecast_24h = output.mean_predictions[0, :24].float().cpu().numpy()

forecast_24h = np.clip(forecast_24h, 0, None)
print(forecast_24h)

Limitations

• Only AQI history is used no weather, traffic, or other covariates.
• Performance may degrade during unexpected pollution events or sensor anomalies.
• The reported metrics are from historical backtesting, not live operation.

Citation

If you use this model, cite the base TimesFM work and this model repository:

@misc{skyloom2026,
  title        = {Skyloom: TimesFM 2.5 for Karachi AQI Forecasting},
  author       = {Mahwiz khalil},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/mahwizzzz/skyloom}}
}