The dataset for this competition comprises tens of thousands of synthetic univariate time series, each containing approximately 1,000 to 5,000 values with a designated boundary point. For each training time series, a label (True for break, False for no break) indicates whether a structural break occurred at this boundary point.

The time series in this competition are designed to represent various real-world scenarios where structural breaks may occur, with different levels of difficulty in detection. This includes scenarios similar to those found in financial markets, climate data, industrial sensor readings, and biomedical signals, among others. The challenge is to develop algorithms that can generalize across these scenarios and accurately detect structural breaks in new, unseen data.

Data Format

For the training data, the X variable will be provided as a pandas.DataFrame with a MultiIndex structure. Here's an example of the format:


                    value  period

  id     time

  0      0       0.001858      0

         1      -0.001664      0

         2      -0.004386      0

         3       0.000699      0

         4      -0.002433      0

  ...              ...        ...

  10000  1890   -0.005903      1

         1891    0.007295      1

         1892    0.003527      1

         1893    0.007218      1

         1894    0.000034      1

The DataFrame has the following structure:

A MultiIndex with two levels:
- id: Identifies the unique time series (each time series has a unique ID)
- time: The timestep within each time series
The columns include:
- value: The actual time series value at that timestep;
- period: A binary indicator where 0 represents the period before the boundary point, and 1 represents the period after the boundary point. The structural break occurs at the change in value, but it may take some time (values) to become detectable/apparent.

The y variable is a boolean pandas.Series, with id as index, indicating whether a structural break
occurred at the boundary point for that time series (True if there was a break, False otherwise).

The test data will follow the same format. Your code will need to process these time series and generate predictions of the likelihood of a structural break for each unique time series ID.

Data Size

The training set consists of 10,000 datasets.

There will also be another 10,000 new datasets for both the public and private test sets.

The testing data provided for local usage only consists of 100 datasets. Participants must consider that their code will run on 100 times more datasets, with a maximum limit of 15 hours of computing time.

Inference must be performed sequentially. It is not possible to read the entire X_test dataset and predict everything at once. Therefore, the code must be optimized to consume one dataset at a time within the limited computing time.