Generic Timeseries Regression

Table of Contents

• Introduction
• Supported Combinations
• Dataset and Model Details
  • Dataset
  • Model Architecture
  • Input Features
  • Output
• Feature Extraction Configuration
• Steps to Run the Example
• See Also
• Sample Output

Introduction

Generic Timeseries Regression is a hello world example for understanding usage of regression AI models on TI MCU. This example uses a simple synthetic regression dataset where the target variable y = 1.2 sin(x) + 3.2 cos(x), with x randomly generated in the range [0, 3]. This serves as a fundamental example for deploying regression models on AM26x microcontrollers.

Supported Combinations

Parameter	Value
CPU + OS	r5fss0-0 nortos
Toolchain	ti-arm-clang
Board	am261x-lp
Example folder	examples/ai/generic_timeseries_regression/

Dataset and Model Details

Dataset

A synthetic regression dataset where:

• x is randomly generated in the range [0, 3]
• Target variable y = 1.2 sin(x) + 3.2 cos(x)

Column Name	Description
x	Randomly generated input in range [0,3]
y	Target: y = 1.2 sin(x) + 3.2 cos(x)

Model Architecture

This lightweight regression model contains approximately 1,800 parameters with one input batch normalization layer, 4 convolution layers, and 2 fully connected layers.

Input Features

The model takes 4D input (N,C,H,W):

• N (1): batch size which is restricted to 1
• C (1): channels which is 1 for signal
• H (10): samples of timeseries signals which is 10 in this example
• W (1): width of samples is restricted to 1 for timeseries applications

Output

This model produces a 1D output representing the predicted values of target variable y.

Feature Extraction Configuration

For this regression task, a SimpleWindow transformation is used, which makes use of the previous frame_size datapoints for prediction of the current target value. A frame size of 10 is used in this example.

No FFT or frequency-domain feature extraction is applied - the model learns directly from raw signal values.

Steps to Run the Example

• When using CCS projects to build, import the CCS project for the required combination and build it using the CCS project menu (see Using SDK with CCS Projects).
• When using makefiles to build, note the required combination and build using make command (see Using SDK with Makefiles)
• Launch a CCS debug session and run the executable, see CCS Launch, Load and Run
• The application will run inference on test vectors and compare against expected outputs

See Also

AI Examples

Sample Output

Generic Timeseries Regression Example Started ...
All tests have passed!!
Golden vectors matched: 3 not matched: 0