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Thesis Stuff/Notes/TODOs

This is a repo dedicated to my MSc Thesis

Abstract (to be filled ...)

Instructions

  • Clone the repository using
git clone https://github.com/alexliap/fts_explore.git
  • to be filled ...

✅ TODO

  • Find univariate dataset for first tests

  • Finetune MOIRAI on this dataset in order to beat on train and validation

  • Find a univariate dataset of a subdomain of the first one and test thesis hypothesis

    • Make iterative train loop
    • Test 3 different configurations for finetuning:
      • Use model of training iteration N-1 for training iteration N. 1 backward pass.
      • At each training iteration use the Stage 1 model for finetuning. 1 backward pass.
      • At each training iteration use the Stage 1 model for finetuning. Multiple backward passes.
        • Without dropout
        • With 10% dropout
        • With 20% dropout
    • For Stage 2, also perform iterative training of the pretrained model in order to test the hypothesis
      • Without dropout
      • With 20% dropout
    • Experiment with different ways of evaluation/visualization
      • Get forecasts and targets, in order to calculate whichever metric

  • Create pipeline where all the above steps are excecuted with the use of Hydra configuration files.

    • Create pipeline for Stage 1
    • Create pipeline for Stage 2
    • Test pipeline end to end

  • Search for other domains & subdomains to make experiments

    • Daily crpto data (BTC & ETH)
      • Split train/validation data to 2022-01-01 => 1004 data points for validation for Stage 1
    • Weather hourly & daily data from Open Meteo (Athens & Smunri)
      • Daily: Train 2014-2021 | Validation 2021-2024

  • Add WQL as a comparison metric. WQL stands for weight quantile loss and is implemented by gluonTS as MeanWeightedSumQuantileLoss. It says mean beacuse it computes WeightedSumQuantileLoss for several quantiles and then calcualtes the mean.