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Dingsu Wang
2020-12-17 14:43:21 +08:00
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2
examples/benchmarks/README.md
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@@ -29,4 +29,4 @@ The numbers shown below demonstrate the performance of the entire `workflow` of
| GRU (Kyunghyun Cho, et al.) | Alpha158 (with selected 20 features) | 0.0311±0.00 | 0.2418±0.04| 0.0425±0.00 | 0.3434±0.02 | 0.0330±0.02 | 0.4805±0.30| -0.1021±0.02 |
| LSTM (Sepp Hochreiter, et al.) | Alpha158 (with selected 20 features) | 0.0312±0.00 | 0.2394±0.04| 0.0418±0.00 | 0.3324±0.03 | 0.0298±0.02 | 0.4198±0.33| -0.1348±0.03 |
| ALSTM (Yao Qin, et al.) | Alpha158 (with selected 20 features) | 0.0385±0.01 | 0.3022±0.06| 0.0478±0.00 | 0.3874±0.04 | 0.0486±0.03 | 0.7141±0.45| -0.1088±0.03 |
| GATs (Petar Velickovic, et al.) | Alpha158 (with selected 20 features) | 0.0349±0.00 | 0.2511±0.01| 0.0457±0.00 | 0.3537±0.01 | 0.0578±0.02 | 0.8221±0.25| -0.0824±0.02 |
| GATs (Petar Velickovic, et al.) | Alpha158 (with selected 20 features) | 0.0349±0.00 | 0.2511±0.01| 0.0457±0.00 | 0.3537±0.01 | 0.0578±0.02 | 0.8221±0.25| -0.0824±0.02 |

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examples/benchmarks/SFM/README.md
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@@ -1,3 +1,3 @@
# State-Frequency-Memory
- State Frequency Memory (SFM) is a novel recurrent network that uses Discrete Fourier Transform to decompose the hidden states of memory cells and capture the multi-frequency trading patterns from past market data to make stock price predictions.
- Paper: Stock Price Prediction via Discovering Multi-Frequency Trading Patterns. [https://www.cs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.](https://www.cs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.)
- Paper: Stock Price Prediction via Discovering Multi-Frequency Trading Patterns. [http://www.eecs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.](http://www.eecs.ucf.edu/~gqi/publications/kdd2017_stock.pdf)

2
examples/benchmarks/TFT/expt_settings/configs.py
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@@ -25,7 +25,7 @@ import os
import data_formatters.qlib_Alpha158
class ExperimentConfig(object):
class ExperimentConfig:
"""Defines experiment configs and paths to outputs.
Attributes:

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examples/benchmarks/TFT/libs/tft_model.py
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@@ -320,7 +320,7 @@ class InterpretableMultiHeadAttention:
return outputs, attn
class TFTDataCache(object):
class TFTDataCache:
"""Caches data for the TFT."""
_data_cache = {}
@@ -348,7 +348,7 @@ class TFTDataCache(object):
# TFT model definitions.
class TemporalFusionTransformer(object):
class TemporalFusionTransformer:
"""Defines Temporal Fusion Transformer.
Attributes:
@@ -972,7 +972,7 @@ class TemporalFusionTransformer(object):
valid_quantiles = self.quantiles
output_size = self.output_size
class QuantileLossCalculator(object):
class QuantileLossCalculator:
"""Computes the combined quantile loss for prespecified quantiles.
Attributes: