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wassname
2022-11-22 21:54:58 +08:00
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commit fc1a605b01
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experiments/configs/ECL/192M.gin
-37
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build.experiment_name = 'ECL/192M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'electricity/electricity.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 192
ForecastDataset.lookback_mult = 5
experiments/configs/ECL/336M.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'ECL/336M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'electricity/electricity.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 336
ForecastDataset.lookback_mult = 3
experiments/configs/ECL/720M.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'ECL/720M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'electricity/electricity.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 720
ForecastDataset.lookback_mult = 1
experiments/configs/ECL/96M.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'ECL/96M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'electricity/electricity.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 96
ForecastDataset.lookback_mult = 9
experiments/configs/ETTm2/192M.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'ETTm2/192M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'ETT-small/ETTm2.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 192
ForecastDataset.lookback_mult = 5
experiments/configs/ETTm2/192S.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'ETTm2/192S'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'ETT-small/ETTm2.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 192
ForecastDataset.lookback_mult = 3
experiments/configs/ETTm2/336M.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'ETTm2/336M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'ETT-small/ETTm2.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 336
ForecastDataset.lookback_mult = 3
experiments/configs/ETTm2/336S.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'ETTm2/336S'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'ETT-small/ETTm2.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'S'
ForecastDataset.horizon_len = 336
ForecastDataset.lookback_mult = 7
experiments/configs/ETTm2/720M.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'ETTm2/720M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'ETT-small/ETTm2.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 720
ForecastDataset.lookback_mult = 1
experiments/configs/ETTm2/720S.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'ETTm2/720S'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'ETT-small/ETTm2.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'S'
ForecastDataset.horizon_len = 720
ForecastDataset.lookback_mult = 1
experiments/configs/ETTm2/96M.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'ETTm2/96M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'ETT-small/ETTm2.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 96
ForecastDataset.lookback_mult = 7
experiments/configs/ETTm2/96S.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'ETTm2/96S'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'ETT-small/ETTm2.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'S'
ForecastDataset.horizon_len = 96
ForecastDataset.lookback_mult = 5
experiments/configs/Exchange/192M.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'Exchange/192M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'exchange_rate/exchange_rate.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 192
ForecastDataset.lookback_mult = 5
experiments/configs/Exchange/192S.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'Exchange/192S'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'exchange_rate/exchange_rate.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'S'
ForecastDataset.horizon_len = 192
ForecastDataset.lookback_mult = 1
experiments/configs/Exchange/336M.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'Exchange/336M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'exchange_rate/exchange_rate.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 336
ForecastDataset.lookback_mult = 7
experiments/configs/Exchange/336S.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'Exchange/336S'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'exchange_rate/exchange_rate.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'S'
ForecastDataset.horizon_len = 336
ForecastDataset.lookback_mult = 7
experiments/configs/Exchange/720M.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'Exchange/720M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'exchange_rate/exchange_rate.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 720
ForecastDataset.lookback_mult = 3
experiments/configs/Exchange/720S.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'Exchange/720S'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'exchange_rate/exchange_rate.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'S'
ForecastDataset.horizon_len = 720
ForecastDataset.lookback_mult = 3
experiments/configs/Exchange/96M.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'Exchange/96M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'exchange_rate/exchange_rate.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 96
ForecastDataset.lookback_mult = 1
experiments/configs/Exchange/96S.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'Exchange/96S'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'exchange_rate/exchange_rate.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'S'
ForecastDataset.horizon_len = 96
ForecastDataset.lookback_mult = 3
experiments/configs/ILI/24M.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'ILI/96M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'illness/national_illness.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 24
ForecastDataset.lookback_mult = 9
experiments/configs/ILI/36M.gin
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@@ -1,37 +0,0 @@
build.experiment_name = 'ILI/192M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'illness/national_illness.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 36
ForecastDataset.lookback_mult = 7
experiments/configs/ILI/48M.gin
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@@ -1,37 +0,0 @@
build.experiment_name = 'ILI/336M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'illness/national_illness.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 48
ForecastDataset.lookback_mult = 5
experiments/configs/ILI/60M.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'ILI/720M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'illness/national_illness.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 60
ForecastDataset.lookback_mult = 3
experiments/configs/Stocks/96M2S.gin
+6 -4
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@@ -23,15 +23,17 @@ Checkpoint.patience = 7
deeptime3.layer_size = 32
deeptime3.inr_layers = 5
deeptime3.n_fourier_feats = 4096
deeptime3.dropout = 0.3
deeptime3.base_learner = 'Ridge'
deeptime3.n_fourier_feats = 2048
deeptime3.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'stocks/OXY_2019.csv.gz'
ForecastDataset.target = 'RSMKs_18_144_72'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.time_features = 'h'
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M2S'
ForecastDataset.horizon_len = 96
ForecastDataset.lookback_mult = 1
ForecastDataset.horizon_len = 46
ForecastDataset.lookback_mult = 2
experiments/configs/Traffic/192M.gin
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@@ -1,37 +0,0 @@
build.experiment_name = 'Traffic/192M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'traffic/traffic.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 192
ForecastDataset.lookback_mult = 5
experiments/configs/Traffic/336M.gin
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@@ -1,37 +0,0 @@
build.experiment_name = 'Traffic/336M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'traffic/traffic.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 336
ForecastDataset.lookback_mult = 5
experiments/configs/Traffic/720M.gin
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@@ -1,37 +0,0 @@
build.experiment_name = 'Traffic/720M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'traffic/traffic.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 720
ForecastDataset.lookback_mult = 3
experiments/configs/Traffic/96M.gin
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@@ -1,37 +0,0 @@
build.experiment_name = 'Traffic/96M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'traffic/traffic.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 96
ForecastDataset.lookback_mult = 9
experiments/configs/Weather/192M.gin
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@@ -1,37 +0,0 @@
build.experiment_name = 'Weather/192M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'weather/weather.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 192
ForecastDataset.lookback_mult = 7
experiments/configs/Weather/336M.gin
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@@ -1,37 +0,0 @@
build.experiment_name = 'Weather/336M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'weather/weather.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 192
ForecastDataset.lookback_mult = 3
experiments/configs/Weather/720M.gin
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@@ -1,37 +0,0 @@
build.experiment_name = 'Weather/720M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'weather/weather.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 720
ForecastDataset.lookback_mult = 5
experiments/configs/Weather/96M.gin
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@@ -1,37 +0,0 @@
build.experiment_name = 'Weather/96M'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'weather/weather.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M'
ForecastDataset.horizon_len = 96
ForecastDataset.lookback_mult = 9
experiments/configs/hp_search/ECL.gin
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@@ -1,37 +0,0 @@
build.experiment_name = 'hp_search/ECL'
build.module = 'experiments.forecast'
build.repeat = 3
build.variables_dict = {
'ForecastDataset.lookback_mult': [1, 3, 5, 7, 9],
'ForecastDataset.horizon_len': [96, 192, 336, 720],
'ForecastDataset.features': ['M'],
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'electricity/electricity.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
experiments/configs/hp_search/ETTm2.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'hp_search/ETTm2'
build.module = 'experiments.forecast'
build.repeat = 3
build.variables_dict = {
'ForecastDataset.lookback_mult': [1, 3, 5, 7, 9],
'ForecastDataset.horizon_len': [96, 192, 336, 720],
'ForecastDataset.features': ['M', 'S'],
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'ETT-small/ETTm2.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
experiments/configs/hp_search/Exchange.gin
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@@ -1,37 +0,0 @@
build.experiment_name = 'hp_search/Exchange'
build.module = 'experiments.forecast'
build.repeat = 3
build.variables_dict = {
'ForecastDataset.lookback_mult': [1, 3, 5, 7, 9],
'ForecastDataset.horizon_len': [96, 192, 336, 720],
'ForecastDataset.features': ['M', 'S'],
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'exchange_rate/exchange_rate.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
experiments/configs/hp_search/ILI.gin
-37
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@@ -1,37 +0,0 @@
build.experiment_name = 'hp_search/ILI'
build.module = 'experiments.forecast'
build.repeat = 3
build.variables_dict = {
'ForecastDataset.lookback_mult': [1, 3, 5, 7, 9],
'ForecastDataset.horizon_len': [24, 36, 48, 60],
'ForecastDataset.features': ['M'],
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'illness/national_illness.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
experiments/configs/hp_search/Stocks.gin
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@@ -0,0 +1,47 @@
build.experiment_name = 'Stocks/96M2S'
build.module = 'experiments.forecast'
build.repeat = 1
build.variables_dict = {
# 'ForecastDataset.lookback_mult': [1, 3, 5, 7, 9],
# 'ForecastDataset.horizon_len': [6, 12, 24, 48, 96, 192, 336, 720],
# 'ForecastDataset.features': ['m', 'h', 'd'],
'deeptime3.base_learner': ['Ridge', 'None', 'Transformer'],
'deeptime3.inr': ['INR', 'INRPlus2'],
'deeptime3.encoder': ['inception', ],
# 'deeptime3.dropout': [0.0, 0.1, 0.3, 0.5,],
}
instance.model_type = 'deeptime3'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime3.layer_size = 32
deeptime3.inr_layers = 5
deeptime3.dropout = 0.3
deeptime3.base_learner = 'Ridge'
deeptime3.n_fourier_feats = 2048
deeptime3.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'stocks/OXY_2019.csv.gz'
ForecastDataset.target = 'RSMKs_18_144_72'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
# ForecastDataset.time_features = 'h'
ForecastDataset.normalise_time_features = True
ForecastDataset.features = 'M2S'
ForecastDataset.horizon_len = 46
ForecastDataset.lookback_mult = 2
experiments/configs/hp_search/Traffic.gin
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@@ -1,37 +0,0 @@
build.experiment_name = 'hp_search/Traffic'
build.module = 'experiments.forecast'
build.repeat = 3
build.variables_dict = {
'ForecastDataset.lookback_mult': [1, 3, 5, 7, 9],
'ForecastDataset.horizon_len': [96, 192, 336, 720],
'ForecastDataset.features': ['M'],
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'traffic/traffic.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True
experiments/configs/hp_search/Weather.gin
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@@ -1,37 +0,0 @@
build.experiment_name = 'hp_search/Weather'
build.module = 'experiments.forecast'
build.repeat = 3
build.variables_dict = {
'ForecastDataset.lookback_mult': [1, 3, 5, 7, 9],
'ForecastDataset.horizon_len': [96, 192, 336, 720],
'ForecastDataset.features': ['M'],
}
instance.model_type = 'deeptime'
instance.save_vals = False
get_optimizer.lr = 1e-3
get_optimizer.lambda_lr = 1.
get_optimizer.weight_decay = 0.
get_scheduler.warmup_epochs = 5
get_data.batch_size = 256
train.loss_name = 'mse'
train.epochs = 50
train.clip = 10.
Checkpoint.patience = 7
deeptime.layer_size = 256
deeptime.inr_layers = 5
deeptime.n_fourier_feats = 4096
deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
ForecastDataset.data_path = 'weather/weather.csv'
ForecastDataset.target = 'OT'
ForecastDataset.scale = True
ForecastDataset.cross_learn = False
ForecastDataset.time_features = []
ForecastDataset.normalise_time_features = True