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Benchstreet is a curated collection of time series prediction models designed to help developers evaluate and compare the performance of different approaches in one-shot, long-term financial data forecasting.

The models are trained on 20 years of S&P 500 daily closing prices provided by Investing.com.

Important

This is not an objective benchmark! It's intended as a qualitative guide and a reference on how to implement these models.

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Table of Contents

Model Type
Transformer/Foundation Models	TimesFM (baseline • fine-tuned), Chronos (baseline • fine-tuned)
Feedforward Neural Networks (FNNs)	MLP (recursive • vector), N-BEATS (direct)
Convolutional Neural Networks (CNNs)	1D-CNN (recursive • vector), TemporalCN (vector)
Recurrent Neural Networks (RNNs)	LSTM (recursive • vector • encoder-decoder), GRU (recursive • vector)
Statistical Models	ARIMA (recursive), SARIMAX (vector), FBProphet (direct)

Want a model added to this list? Raise an issue here or make a PR!

Tip

The winner: N-BEATS. High accuracy with extremely low training time.

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TimesFM

timesfm-baseline

timesfm/baseline.py

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timesfm-fine-tuned

timesfm/fine_tune.py • download on huggingface 🤗

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Chronos

chronos-baseline

chronos/baseline.py

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chronos-fine-tuned

chronos/fine_tune.ipynb • download on huggingface 🤗

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MLP

mlp-recursive

mlp/recursive.py

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mlp-vector

mlp/vector.py

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N-BEATS

n_beats/direct.py

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1D-CNN

1d-cnn-recursive

cnn/recursive.py

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1d-cnn-vector

cnn/vector.py

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TemporalCN

tcn/vector.py

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LSTM

lstm-recursive

lstm/recursive.py

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lstm-vector

lstm/vector.py

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lstm-encoder-decoder

lstm/encdec.py

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GRU

gru-recursive

gru/recursive.py

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gru-vector

gru/vector.py

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ARIMA

arima/recursive.py

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SARIMAX

sarimax/direct.py

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FBProphet

fbprophet/direct.py

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Want a model added to this list? Raise an issue here or make a PR!