5.3. Forecasting Dataset Format

This guide explains how to format datasets for time series forecasting tasks.

5.3.1. Directory Structure

Forecasting uses the same structure as regression:

my_dataset/
├── files/                          # MUST be named "files"
│   ├── sequence1.csv
│   ├── sequence2.csv
│   └── sequenceN.csv
└── annotations/                    # Required
    ├── instances_train_list.txt
    └── instances_val_list.txt

5.3.2. Data File Format

All variables (features) are in columns. You specify which to use as inputs and which to forecast via configuration.

Example: Temperature Forecasting

ambient,coolant,current,pm_temp
19.85,18.81,2.28,22.93
19.85,18.79,2.28,22.94
19.85,18.79,2.28,22.94
19.85,18.77,2.28,22.94
...

In this example:

Column 0 (ambient): Input feature
Column 1 (coolant): Input feature
Column 2 (current): Input feature
Column 3 (pm_temp): Target to forecast

5.3.3. Key Difference from Regression

Regression: Target is a separate value for each window (in last column)

Forecasting: Target is a future value of an existing variable

Regression: Input [t0...t3] → Predict separate_target_avg
Forecasting: Input [t0, t1, t2] → Predict variable[t3]

5.3.4. Configuration

dataset:
  enable: True
  dataset_name: 'my_forecast_data'
  input_data_path: '/path/to/my_dataset'

data_processing_feature_extraction:
  data_proc_transforms: ['SimpleWindow']
  frame_size: 3                    # Lookback (use 3 past values)
  forecast_horizon: 1              # Predict 1 step ahead
  stride_size: 0.4

  # Specify columns by index or name
  variables: [0, 3]                # Use columns 0 and 3 as inputs
  target_variables: [3]            # Forecast column 3

training:
  model_name: 'FCST_LSTM8'
  output_int: False                # Required for forecasting!

5.3.5. Variable Specification Options

By Column Index (0-based, after time column removal):

variables: [0, 3]           # Use columns 0 and 3
target_variables: [3]       # Forecast column 3

By Column Name (requires CSV header):

variables: ['ambient', 'pm_temp']
target_variables: ['pm_temp']

Multiple Targets (forecast several variables):

variables: [0, 1, 2, 3]
target_variables: [2, 3]     # Forecast columns 2 and 3

5.3.6. Windowing Behavior

With frame_size=3 and forecast_horizon=1:

Data: [v0, v1, v2, v3, v4, v5, v6, ...]

Window 1: Input [v0, v1, v2] → Output [v3]
Window 2: Input [v1, v2, v3] → Output [v4]
Window 3: Input [v2, v3, v4] → Output [v5]
...

5.3.7. Complete Example

Dataset structure:

pmsm_temp_forecast/
├── files/
│   ├── profile_10.csv
│   ├── profile_11.csv
│   └── profile_12.csv
└── annotations/
    ├── instances_train_list.txt
    └── instances_val_list.txt

profile_10.csv:

ambient,coolant,u_d,u_q,i_a,pm
19.850,18.815,1.499,0.032,2.281,22.936
19.850,18.793,1.542,-0.092,2.281,22.941
19.850,18.790,1.456,0.081,2.281,22.944
...

config.yaml:

common:
  task_type: 'generic_timeseries_forecasting'
  target_device: 'F28P55'

dataset:
  dataset_name: 'pmsm_temp'
  input_data_path: '/data/pmsm_temp_forecast'

data_processing_feature_extraction:
  data_proc_transforms: ['SimpleWindow']
  frame_size: 3
  forecast_horizon: 1
  stride_size: 0.4
  variables: ['ambient', 'pm']      # Use ambient and pm as inputs
  target_variables: ['pm']           # Forecast pm temperature

training:
  model_name: 'FCST_LSTM8'
  output_int: False

5.3.8. Important Notes

Warning

output_int must be False for forecasting
Feature extraction (FFT, wavelets) is not supported
The target variable should typically be included in input variables

5.3.9. Minimum Data Requirements

Each file must have at least:

frame_size + forecast_horizon

rows to generate at least one training sample.

5.3.10. Common Issues

“Insufficient sequence length” error

Files need at least frame_size + forecast_horizon rows.

Poor forecasting performance

Increase frame_size to capture more history
Try LSTM models for complex temporal patterns
Ensure sufficient training data