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title: DATFID MASTER
emoji: 🤖
colorFrom: indigo
colorTo: pink
sdk: docker
pinned: false
short_description: DATFID - Secure and powerful forecasting SDK
---
# DATFID MASTER
**DATFID** delivers reliable and interpretable forecasting for any time series. Our models consistently rank at the top—see our website [datfid.com](https://datfid.com) for live results.
We achieved **state-of-the-art accuracy in the M5 Forecasting Competition**, beated all the benchmarks, forecasting hierarchical Walmart product sales over a 5-year period using advanced exogenous features and rigorous error metrics ([Kaggle M5 page](https://www.kaggle.com/c/m5-forecasting-accuracy)).
---
## Why DATFID Stands Out
- **Professionally recognized benchmarking**
M5 is part of the renowned “M-Competitions” series, used worldwide to compare forecasting methods under realistic conditions. ([Makridakis Competitions, Wikipedia](https://en.wikipedia.org/wiki/Makridakis_Competitions)).
- **Hierarchical & intermittent series**
DATFID handles complex retail data with product-store hierarchies, zero-heavy series, pricing, calendar events, and promotional features—all core challenges of the M5 setup ([Background & Organization of the M5 Competition](https://www.sciencedirect.com/science/article/pii/S0169207021001187)).
- **Robust accuracy**
Scoring well on 42,840 series across multiple aggregation levels is a testament to our method’s strength—especially on challenging intermittent product-level data.
- **Fast, scalable, and interpretable**
Unlike “black-box” approaches, we prioritize transparency and efficiency without sacrificing accuracy.
---
## Getting Started
### 1. Request a Token
To access the API, request your personal DATFID token via **admin@datfid.com**.
### 2. Install the SDK
```bash
pip install -i https://test.pypi.org/simple/ datfid
```
### 3. Example Usage
```python
import pandas as pd
from datfid.client import DATFIDClient
# Initialize with your DATFID token
client = DATFIDClient(token="dt+YOUR_TOKEN_HERE")
# Fit the model
fit_result = client.fit_model(
df=my_dataframe,
id_col="SKU_ID",
time_col="Date",
y="Sales",
lagged_features={"Sales": 2, "Promo": 1},
current_features=["Price", "Holiday"],
filter_by_significance=True,
meanvar_test=True
)
# Forecast
forecast_df = client.forecast_model(df_forecast=my_forecast_dataframe)
print(forecast_df.head())
# Check token validity
client.secure_ping()
```
---
## Contact & Info
- Website: [datfid.com](https://datfid.com)
- Get your token: **admin@datfid.com**
---
Happy forecasting with confidence!
— The DATFID Team |