How to tune the parameters of detection algorithms?

[JR-Jared]

I use roerich on my own dataset, but the result is not so good.

There are comments in the class ChangePointDetection:

    Parameters
    ----------
    scaler: A scaler object is used to scale an input data. The default one is `SmaScalerCache`
    metric: A loss function during optimize step of NN. Can be one of the following KL_sym, KL, JSD, PE, PE_sym, Wasserstein
    window_size: A size of a window when splitting input data into train and test arrays
    periods: A number of previous data-points used when constructing autoregressive matrix
    lag_size: A distance between train- and test- windows
    step: Each `step`-th data-point is used when creating the input dataset
    n_epochs: A number of epochs during training NN
    lr: A learning rate at each step of optimizer
    lam: A regularization rate
    optimizer: One of Adam, SGD, RMSprop or ASGD optimizers
    debug: default zero

How to tune the parameters of the change point algorithms, like periods, window_size , lag_size or step?

[hushchyn-mikhail]

@JR-Jared, hi!

The key parameters here are window_size and lag_size. The general recommendation is to choose values such that only one change point is within 2 * (window_size + lag_size).