Multivariate Singular Spectrum (mSSA) is an algorithm for multivariate time series forecasting and imputation. Specifically, mSSA allows you to predict entries that are:

1. At a future time step (i.e. forecasting);

2. Missing/corrupted by noise (i.e. imputation)

This repository is the implementation of the paper: On Multivariate Singular Spectrum Analysis. Refer to the paper for more information about the theory and the algorithm of mSSA.  

This work has the following dependencies:

• Python 3.5+ with the libraries: (numpy, pandas, scipy, sklearn)

To install the mSSA package form the source, simply clone this repository and then install the package using pip as follows:

pip3 install .

To get started, first load the time series example we have provided in ../mSSA/examples/testdata/tables/mixturets_var.csv using pandas.

import pandas as pd
df = pd.read_csv("mssa/examples/testdata/tables/mixturets_var.csv")

Then initialise and fit your mSSA model on the time series named ts as follows:

from mssa.mssa import mSSA
model = mSSA()
model.update_model(df.loc[:,['ts']]) 

Then you can impute or forecast any entry using the predict function. For example:

prediction = model.predict('ts',1000)

will impute the 1000th entry, while

prediction = model.predict('ts', 100001,100100)

will forecast the entries between 100001 to 100100.

Refer to the documentation of the mSSA class in here.

We provide a running example for both synthetic and real-world datasets in a python notebook in the mssa/examples folder. Here.

This work is licensed under the Apache 2.0 License.