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Invited Session: MCM 2019

I am delighted to serve on the Program Committee of the Twelfth International Conference on Monte Carlo Methods and Application (MCM 2019), to be held in Sydney, Australia, from July 8 to 12, 2019.

I am organizing a special thematic session (of either 3 or 4 talks of 30 minutes each) in Monte Carlo Methods for Large Dependent Data. If you are interested in contributing a talk, please let me know.

Confirmed Speakers:

  • Clara Grazian, University of Oxford
  • Ruben Loaiza-Maya, Monash University
  • Yanfei Kang, Beihang University
  • Feng Li, Central University of Finance and Economics

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Paper

New Paper: Distributed ARIMA Models for Ultra-long Time Series

Authors:  Xiaoqian WangYanfei Kang, Rob J Hyndman and Feng Li

Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management. This paper develops a novel distributed forecasting framework to tackle challenges associated with forecasting ultra-long time series by utilizing the industry-standard MapReduce framework. The proposed model combination approach facilitates distributed time series forecasting by combining the local estimators of ARIMA (AutoRegressive Integrated Moving Average) models delivered from worker nodes and minimizing a global loss function. In this way, instead of unrealistically assuming the data generating process (DGP) of an ultra-long time series stays invariant, we make assumptions only on the DGP of subseries spanning shorter time periods. We investigate the performance of the proposed distributed ARIMA models on an electricity demand dataset. Compared to ARIMA models, our approach results in significantly improved forecasting accuracy and computational efficiency both in point forecasts and prediction intervals, especially for longer forecast horizons. Moreover, we explore some potential factors that may affect the forecasting performance of our approach.

Links: Working Paper | Spark Implementation

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The forecasting with time series imaging paper is accepted in Expert Systems with Applications

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The GRATIS paper is accepted in Statistical Analysis and Data Mining