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Gluonts Darts Pytorch Forecasting

General-purpose pre-trained forecasting models are rare due to the domain-specific nature of forecasting and varying data structures. Emerging domain-specific models exist for niches like retail, finance, and healthcare, while pre-trained architectures combined with transfer learning offer practical solutions. Cloud-based AutoML tools provide black-box forecasting engines, but unique data patterns and regulatory needs may necessitate custom training approaches.

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Adel Hlilou
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37 views2 pages

Gluonts Darts Pytorch Forecasting

General-purpose pre-trained forecasting models are rare due to the domain-specific nature of forecasting and varying data structures. Emerging domain-specific models exist for niches like retail, finance, and healthcare, while pre-trained architectures combined with transfer learning offer practical solutions. Cloud-based AutoML tools provide black-box forecasting engines, but unique data patterns and regulatory needs may necessitate custom training approaches.

Uploaded by

Adel Hlilou
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© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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1.

General-Purpose Pre-Trained Models (Rare)

Forecasting is highly domain-specific and data-dependent. Unlike NLP (e.g., BERT, GPT) or computer
vision (e.g., ResNet), universal "one-size-fits-all" pre-trained forecasting models don’t exist. Why?

 Historical patterns in retail, energy, or finance rarely transfer across domains.

 Data structures (time granularity, seasonality, external factors) vary too much.

2. Domain-Specific Pre-Trained Models (Emerging)

Some niches offer pre-trained models fine-tuned on common datasets:

Domain Examples Use Case

Retail/Supply Amazon Forecast’s built-in algorithms, GluonTS Demand forecasting,


Chain models (e.g., DeepAR, Transformer) inventory optimization

Stock trends, volatility


Finance Prophet (Meta), AutoTS
prediction

Energy N-BEATS, Temporal Fusion Transformers (TFT) Electricity load forecasting

Epidemic-specific models (e.g., ARGO for flu


Healthcare Disease outbreak prediction
trends)

3. Pre-Trained Architectures + Transfer Learning

This is the most practical approach:

1. Start with a model architecture pre-trained on large-scale temporal data (e.g., an LSTM or
Transformer trained on diverse time-series datasets).

2. Fine-tune it on your specific data (even with limited samples).


→ Tools enabling this:

o GluonTS: Library with pre-defined models (DeepAR, Transformer).

o Darts: Includes PyTorch-based models (N-BEATS, TFT).

o PyTorch Forecasting: Offers TFT and N-HiTS.

4. Cloud-Based "Pretrained" APIs (AutoML)

Major cloud platforms offer "black-box" forecasting engines that behave like pre-trained systems:

 Amazon Forecast: Auto-trains ensembles of algorithms on your data.


 Google Vertex AI Time Series: Uses AutoML or pre-built ARIMA/PROPHET.

 Microsoft Azure Anomaly Detector: Includes forecasting capabilities.


These handle model selection/training behind the scenes but still require your data for
customization.

5. When Are Pre-Trained Models Not Suitable?

Scenario Better Approach

Highly unique data patterns Train from scratch

Regulatory needs (e.g.,


Use simpler models (ARIMA, ETS)
explainability)

Small datasets Statistical models or transfer learning

Real-time/low-latency needs Lightweight models (e.g., Exponential Smoothing)

Key Considerations

 Data Similarity Matters: A model pre-trained on retail sales won’t work for predicting ICU
admissions.

 Fine-Tuning is Key: Even "pre-trained" forecasting models need calibration with your data.

 Open-Source > Truly Pre-Trained: Libraries like GluonTS provide architectures, not weights.
You train them on your data.

 Hybrid Approach: Combine pre-trained components (e.g., feature extractors) with custom
heads.

Bottom Line

While you won’t find "downloadable" forecasting models like BERT or ResNet, pre-defined
architectures + transfer learning (e.g., GluonTS, Darts) and cloud AutoML tools (e.g., Amazon
Forecast) deliver similar benefits. For niche domains (energy, epidemiology), specialized pre-trained
models are emerging but still require fine-tuning.

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