giswlh / mssa

Multivariate Singular Spectrum Analysis (mSSA): Forecasting and Imputation algorithm for multivariate time series

Multivariate Singular Spectrum Analysis (mSSA)

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.  

Installation

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 .

Getting Started

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.

API

Refer to the documentation of the mSSA class in here.

Example

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

License

This work is licensed under the Apache 2.0 License.