LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts

  title={LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts},
  author={Dilini Sewwandi Rajapaksha and Christoph Bergmeir and Rob J Hyndman},
2 Citations

Advances in Time Series Forecasting Development for Power Systems’ Operation with MLOps

Load forecasts are generated by means of a data-driven based forecasting tool ProLoaF, which is benchmarked with state-of-the-art baseline models and the auto-machine learning models auto.arima and Facebook Prophet to translate a time series forecast method’s quality to its respective business value.



LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series With Multiple Seasonal Patterns

This article proposes long short-term memory multiseasonal net (LSTM-MSNet), a decomposition-based unified prediction framework to forecast time series with multiple seasonal patterns, outperforming many state-of-the-art multise Masonal forecasting methods.

Monash Time Series Forecasting Archive

This paper presents a comprehensive forecasting archive containing 25 publicly available time series datasets from varied domains, with different characteristics in terms of frequency, series lengths, and inclusion of missing values, for the benefit of researchers using the archive to benchmark their forecasting algorithms.

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions


• To forecast future values of a time series is one of the main goals in times series analysis. Many forecasting methods have been developed and its performance evaluated. In order to make a

Forecasting at Scale

A practical approach to forecasting “at scale” that combines configurable models with analyst-in-the-loop performance analysis, and a modular regression model with interpretable parameters that can be intuitively adjusted by analysts with domain knowledge about the time series are described.

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

The proposed deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers has a number of desirable properties, being interpretable, applicable without modification to a wide array of target domains, and fast to train.

Statistical and Machine Learning forecasting methods: Concerns and ways forward

It is found that the post-sample accuracy of popular ML methods are dominated across both accuracy measures used and for all forecasting horizons examined, and that their computational requirements are considerably greater than those of statistical methods.

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks