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darima

Distributed ARIMA Models implemented with Apache Spark

Introduction

DARIMA is designed to facilitate forecasting ultra-long time series by utilizing the industry-standard MapReduce framework. The algorithm is developed on Spark platform and both Python as well as R interfaces.

See darima for developed functions used for implementing DARIMA models.

• model.py : Train ARIMA models for each subseries and convert the trained models into AR representations (Mapper).
• dlsa.py : Combine the local estimators obtained in Mapper by minimizing the global loss function (Reducer).
• forecast.py : Forecast the next H observations by utilizing the combined estimators.
• evaluation.py : Calculate the forecasting accuracy in terms as MASE, sMAPE and MSIS.
• R : R functions designed for modeling, combining and forecasting. rpy2 is needed as an interface to use R from Python.

System requirements

• Spark >= 2.3.1
• Python >= 3.7.0
  • pyspark >= 2.3.1
  • rpy2 >= 3.0.4
  • scikit-learn >= 0.21.2
  • numpy >= 1.16.3
  • pandas >= 0.23.4
• R >= 3.5.2
  • forecast >= 8.5
  • polynom = 1.3.9
  • dplyr >= 0.8.4
  • quantmod >= 0.4.13
  • magrittr >= 1.5

Usage

DARIMA

Run the PySpark code to forecast the time series of the GEFCom2017 by utilizing DARIMA.

  ./bash/run_darima.sh

or simply run

  PYSPARK_PYTHON=/usr/local/bin/python3.7 ARROW_PRE_0_15_IPC_FORMAT=1 spark-submit ./run_darima.py

Note: ARROW_PRE_0_15_IPC_FORMAT=1 is added to instruct PyArrow >= 0.15.0 to use the legacy IPC format with the older Arrow Java that is in Spark 2.3.x and 2.4.x.

ARIMA

Run the R code to forecast the time series of the GEFCom2017 by utilizing the auto.arima() function (used for comparison).

  ./bash/auto_arima.sh

or simply run

  Rscript auto_arima.R

References

• Xiaoqian Wang, Yanfei Kang, Rob J Hyndman, & Feng Li (2022) Distributed ARIMA models for ultra-long time series. International Journal of Forecasting DOI: 10.1016/j.ijforecast.2022.05.001.