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48 lines
1.4 KiB
Plaintext
48 lines
1.4 KiB
Plaintext
build.experiment_name = 'Stocks/96M2S'
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build.module = 'experiments.forecast'
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build.repeat = 1
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build.variables_dict = {
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# 'ForecastDataset.lookback_mult': [1, 3, 5, 7, 9],
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# 'ForecastDataset.horizon_len': [6, 12, 24, 48, 96, 192, 336, 720],
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# 'ForecastDataset.features': ['m', 'h', 'd'],
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'deeptime3.base_learner': ['Ridge', 'None', 'Transformer'],
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'deeptime3.inr': ['INR', 'INRPlus2'],
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'deeptime3.encoder': ['inception', 'lstm', 'mlp', 'lstm2', 'transformer', 'transformer2', 'none'],
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# 'deeptime3.dropout': [0.0, 0.1, 0.3, 0.5,],
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}
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instance.model_type = 'deeptime3'
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instance.save_vals = False
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get_optimizer.lr = 1e-3
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get_optimizer.lambda_lr = 1.
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get_optimizer.weight_decay = 0.
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get_scheduler.warmup_epochs = 5
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get_data.batch_size = 256
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train.loss_name = 'mse'
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train.epochs = 50
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train.clip = 10.
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Checkpoint.patience = 7
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deeptime3.layer_size = 256
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deeptime3.inr_layers = 5
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deeptime3.dropout = 0.1
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deeptime3.base_learner = 'Ridge'
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deeptime3.n_fourier_feats = 4096
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deeptime3.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
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ForecastDataset.data_path = 'stocks/OXY_2019.csv.gz'
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ForecastDataset.target = 'RSMKs_18_144_72'
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ForecastDataset.scale = True
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ForecastDataset.cross_learn = False
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ForecastDataset.time_features = []
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# ForecastDataset.time_features = 'h'
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ForecastDataset.normalise_time_features = True
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ForecastDataset.features = 'M2S'
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ForecastDataset.horizon_len = 46
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ForecastDataset.lookback_mult = 3
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