5.2. Regression Dataset Format

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

5.2.1. Directory Structure

Regression datasets use a flat files/ folder structure (not class folders):

my_dataset/
├── files/                          # MUST be named "files"
│   ├── datafile1.csv
│   ├── datafile2.csv
│   └── datafileN.csv
└── annotations/                    # Required for regression
    ├── instances_train_list.txt
    └── instances_val_list.txt

Important

The data directory must be named files/, not classes/ or anything else.

5.2.2. Data File Format

Critical: The target value must be in the last column.

feature1,feature2,feature3,...,target
0.5,18.2,45.5,...,0.187
0.6,18.5,45.6,...,0.245
...

Example: Motor Torque Prediction

current_d,current_q,voltage_d,voltage_q,motor_speed,pm_temp,target_torque
-0.450,0.032,18.805,1.499,0.002,24.55,0.187
-0.325,0.045,18.818,1.542,0.003,24.54,0.245
-0.440,0.028,18.876,1.456,0.002,24.54,0.176

Columns 1-6: Input features
Column 7 (last): Target value to predict

5.2.3. Time Column Handling

Any column containing “time” is automatically dropped:

Time,feature1,feature2,target
0.001,0.5,18.2,0.187
0.002,0.6,18.5,0.245

The “Time” column will be removed automatically.

5.2.4. Annotation Files (Required)

Unlike classification, regression requires annotation files:

instances_train_list.txt:

datafile1.csv
datafile3.csv
datafile5.csv

instances_val_list.txt:

datafile2.csv
datafile4.csv

instances_test_list.txt (optional):

datafile6.csv

If you don’t provide annotations, ModelMaker will auto-generate them.

5.2.5. Configuration

dataset:
  enable: True
  dataset_name: 'my_regression_data'
  input_data_path: '/path/to/my_dataset'
  data_dir: 'files'
  annotation_dir: 'annotations'

data_processing_feature_extraction:
  data_proc_transforms: ['SimpleWindow']  # Required!
  frame_size: 128
  stride_size: 0.25
  variables: 6                            # Input columns (excluding target)

Important

SimpleWindow transform is required for regression tasks.

5.2.6. Target Processing

The target value (last column) is processed as follows:

Each window of frame_size rows is extracted
The target value is averaged across the window
This averaged value becomes the label for that window

Example with frame_size=4:

Window 1: rows 0-3, targets [0.18, 0.24, 0.17, 0.19] → avg = 0.195
Window 2: rows 2-5, targets [0.17, 0.19, 0.22, 0.20] → avg = 0.195

5.2.7. Complete Example

Dataset structure:

torque_measurement/
├── files/
│   ├── experiment_001.csv
│   ├── experiment_002.csv
│   ├── experiment_003.csv
│   └── experiment_004.csv
└── annotations/
    ├── instances_train_list.txt    # experiment_001.csv, experiment_002.csv
    └── instances_val_list.txt      # experiment_003.csv, experiment_004.csv

experiment_001.csv:

i_d,i_q,u_d,u_q,speed,temp,torque
-0.45,0.03,18.80,1.49,0.002,24.5,0.187
-0.32,0.04,18.81,1.54,0.003,24.5,0.245
...

config.yaml:

common:
  task_type: 'generic_timeseries_regression'
  target_device: 'F28P55'

dataset:
  dataset_name: 'torque_measurement'
  input_data_path: '/data/torque_measurement'

data_processing_feature_extraction:
  data_proc_transforms: ['SimpleWindow']
  frame_size: 128
  stride_size: 0.25
  variables: 6

training:
  model_name: 'REGR_1k_NPU'
  training_epochs: 100

5.2.8. Common Issues

“Target not found” error

Ensure the target is in the last column of your CSV.

“No windows generated” error

Check that files have at least frame_size rows.

Poor regression performance

Try different frame_size values
Ensure input features are relevant to target
Normalize extreme values in your data