Multivariate Singular Spectrum (mSSA) is an algorithm for multivariate time series forecasting and imputation. Specifically, mSSA allows you to predict entries that are:
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At a future time step (i.e. forecasting);
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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.