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Repository for seminar research at Humboldt University of Berlin to evaluate performance of SOTA Transformers models for time series forecasting using benchmark and extended synthetic datasets.

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anasashb/TransformersEnergyTS

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TransformersEnergyTS

Key Directories and Modules

Entire Models Alongside our User-Friendly Wrappers for Training

  • TransformersModels/AutoformerAPI.py
  • TransformersModels/CrossformerAPI.py
  • TransformersModels/FedformerAPI.py
  • TransformersModels/InformerAPI.py
  • TransformersModels/LogSparseAPI.py
  • TransformersModels/TSMixerAPI.py

Synythetic Data Framework

  • data_generator.py

Synthetic Data Framework Demo

  • internal_demos/data_generator_demo.ipynb

Data Plots and Statistical Tests

  • internal_demos/synth_analysis.ipynb

Wind Data Demo

  • internal_demos/wind_data_demo.ipynb

Experiment Results

  • results/

Our Synthetic Data (Extended from Liu et al. 2023, Pyraformer)

Synthetic time series with trends

Forecasting Results

Multivariate

Multivariate forecasting results

Univariate

Univariate forecasting results


For Transparency

Datasets Used in Experiments

  • SYNTHDataset/SYNTHh1.csv
  • SYNTHDataset/SYNTH_additive.csv
  • SYNTHDataset/SYNTH_additive_reversal.csv
  • SYNTHDataset/SYNTH_multiplicative.csv
  • SYNTHDataset/SYNTH_multiplicative_reversal.csv
  • WINDataset/DEWINDh_small.csv
  • ETTDataset/ETTh1.csv

Model Training and Forecasting Logs

  • new_forecast_notebook.ipynb

Results Analysis

  • results_analysis.ipynb

(Outdated) Prototype

  • prototype.ipynb

Note that results, data and code in the Prototype have since been reworked in the newer modules and directories.

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Repository for seminar research at Humboldt University of Berlin to evaluate performance of SOTA Transformers models for time series forecasting using benchmark and extended synthetic datasets.

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