Implementation of the algorithm described in: SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise, by Abdullah Alomar, Munther Dahleh, Sean Mann, and Devavrat Shah (published in NeurIPS 2023). See the paper in here.

The implementation has a fairly straightforward interface. First, import SAMoSSA using,

from samossa import SAMoSSA

The algorithm takes as input $T$ observations of a multivariate time series with dimension $N$ in the form of a Numpy array. For example:

# load data and init model
data = np.load("datasets/electricity/electricity.npy")
T, N = data.shape

Then to initialize the model, you would need to specify at least two parameters: numSeries (int): the dimension of the multivariate time series; and L (int): the number of lags used in the linear model (check the example, the code and the paper for guidance about choice of L).

# choose L such that it is on the order of (sqrt(NT)) if N < T, otherwise, choose it to be on of order ~T (must be less than T in this case!)
L = int(np.sqrt(N*T/4))
model = SAMoSSA(N, L, )

Then use .fit() to train the model, and .predict(h) to forecast the next h entries in the time series.

# fit model on all but the last 24 steps
model.fit(data[:-24,:])
# forecast the next 24 steps!
predictions = model.predict(24)