Evaluating Time Series Models

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#Machine Learning #Time Series

Evaluating time series models is usually different from most other machine learning tasks as we usually don’t have i.i.d. data.

Out-of-sample

Out-of-Sample with Sliding Window

Out-of-Sample with Sliding Window

If the sliding window size is 1, then we have the simplest out-of-sample holdout scenario.

Prequential

Prequential with Gap

Prequential with Gap

Prequential with Growing Train

Prequential with Growing Train

Prequential with Sliding Blocks

Prequential with Sliding Blocks

Cross-validation

Cross-validation

Cross-validation

Cross-validation with Neighbor removed

Cross-validation with Neighbor removed

Planted: by ;

References:
  1. Cerqueira V, Torgo L, Mozetic I. Evaluating time series forecasting models: An empirical study on performance estimation methods. arXiv [cs.LG]. 2019. Available: http://arxiv.org/abs/1905.11744
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L Ma (2022). 'Evaluating Time Series Models', Datumorphism, 04 April. Available at: https://datumorphism.leima.is/wiki/forecasting/evalutate-time-series-models/.