preset forecasting data used only when default forecast length is set, tested, updated README.md

README.md

@@ -7,4 +7,63 @@ sdk: docker
 app_port: 7860
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# TiRex – Zero‑Shot Time Series Forecasting App
+
+A Gradio‑based interactive web app to perform zero‑shot time series forecasting using the TiRex model. Upload your own CSV/XLSX/Parquet files or choose from built‑in presets, filter series by name, and visualize quantile forecasts over your chosen horizon.
+
+---
+
+## 🔍 Features
+
+- **Zero‑Shot Forecasting**: Powered by the [`NX-AI/TiRex`](https://huggingface.co/NX-AI/TiRex) model.
+- **Custom Data Upload**: Accepts CSV, XLSX, and Parquet.
+- **Preset Datasets**: Includes `loop.csv`, `air_passangers.csv`, and `ett2.csv` for quick demos.
+- **Interactive Filtering**: Search, check/uncheck, and plot only the series you care about.
+- **Quantile Forecasts**: Displays historical data, median forecast line, and 10–90% quantile shading.
+- **Configurable Horizon**: Slider to set forecast length (1–512 steps).
+- **Automatic Defaults**: Detects best forecast‐length defaults for presets.
+
+---
+
+## 📊 Data Format
+
+### With Named Series
+```csv
+AAPL,120.5,121.0,119.8,122.1,123.5,...
+AMZN,3300.0,3310.5,3295.2,3305.8,3315.1,...
+GOOGL,2800.1,2795.3,2810.7,2805.2,2820.4,...
+```
+
+### Without Named Series
+```csv
+120.5,121.0,119.8,122.1,123.5,...
+3300.0,3310.5,3295.2,3305.8,3315.1,...
+2800.1,2795.3,2810.7,2805.2,2820.4,...
+```
+
+### Key Rules:
+- **One row per time series**
+- **Consistent naming**: Either all rows have names (first column) or none do
+- **Numeric data**: All values after the optional name column must be numeric
+- **Minimum length**: Time series must have at least `forecast_length + 10` data points
+- **Maximum constraints**: Up to 30 time series and 2048 time steps per series
+
+## 🔧 Configuration
+
+### Forecast Length
+- **Default**: 64 steps
+- **Range**: 1-512 steps
+- **Auto-adjustment**: Preset datasets have optimized forecast lengths:
+  - `loop.csv` and `ett2.csv`: 256 steps
+  - `air_passangers.csv`: 48 steps
+
+### Model Settings
+- **Device**: CUDA (T4 GPU)
+- **Quantiles**: 10%, 50% (median), 90% prediction intervals
+
+## 📈 Output Features
+
+- **Historical data**: Blue line showing input time series
+- **Median forecast**: Orange line for point predictions
+- **Uncertainty bands**: Gray shaded area showing 10%-90% 
+

app.py

@@ -18,13 +18,13 @@ torch.manual_seed(42)
 model: ForecastModel = load_model("NX-AI/TiRex",device='cuda')
 
 def model_forecast(input_data, forecast_length=256, file_name=None):
-    if os.path.basename(file_name) == "loop.csv":
+    if os.path.basename(file_name) == "loop.csv" and forecast_length==256:
         _forecast_tensor = torch.load("data/loop_forecast_256.pt")
         return _forecast_tensor[:,:forecast_length,:]
-    elif os.path.basename(file_name) == "ett2.csv":
+    elif os.path.basename(file_name) == "ett2.csv" and forecast_length==256:
         _forecast_tensor = torch.load("data/ett2_forecast_256.pt")
         return _forecast_tensor[:,:forecast_length,:]
-    elif os.path.basename(file_name) == "air_passangers.csv":
+    elif os.path.basename(file_name) == "air_passangers.csv"and forecast_length==48:
         _forecast_tensor = torch.load("data/air_passengers_forecast_48.pt")
         return _forecast_tensor[:,:forecast_length,:]
     else:
@@ -413,5 +413,5 @@ with gr.Blocks(fill_width=True,theme=gr.themes.Ocean()) as demo:
 '''
 gradio app.py
 ssh -L 7860:localhost:7860 nikita_blago@oracle-gpu-controller -t \
-   ssh -L 7860:localhost:7860 compute-permanent-node-368
+   ssh -L 7860:localhost:7860 compute-permanent-node-195
 '''