Garethmd's workspace
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126
Name
48 visualized
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Runtime
Sweep
batch_size
batches_per_epoch
context_length
conts
dataset
dropout
epochs
filename
freq
hidden_dim
input_dim
lag_seq
lr
n_layers
output_dim
patience
prediction_length
rnn_type
scaled_covariates
seasonality
seed
training_method
abs_error
abs_target_mean
abs_target_sum
epoch
mae
mape
mase
mse
nd
rmse
smape
train_loss
Finished
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garethmd
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32
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["unix_timestamp"]
tourism
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
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lstm
["unix_timestamp"]
12
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TrainingMethod.TEACHER_FORCING
18345184
17803.80273
156388592
100
2088.47754
0.21758
1.60569
82601896
0.20609
2709.92554
0.19997
18.67134
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garethmd
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["month"]
tourism
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
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lstm
["month"]
12
45
TrainingMethod.TEACHER_FORCING
19408558
17521.03711
153904784
100
2209.53516
0.22518
1.65059
94935656
0.21353
2857.07935
0.20379
18.72721
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garethmd
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32
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["unix_timestamp"]
tourism
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["unix_timestamp","feat_scale"]
12
46
TrainingMethod.TEACHER_FORCING
18390634
17667.74414
155193440
100
2093.65137
0.22496
1.6551
68548080
0.21698
2671.81372
0.20413
18.34002
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garethmd
15
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["month","unix_timestamp"]
tourism
0.1
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["month","unix_timestamp","feat_scale"]
12
44
TrainingMethod.TEACHER_FORCING
18323788
17891.20313
157156352
100
2086.04126
0.22399
1.6538
70974528
0.21778
2692.31543
0.20416
21.86629
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garethmd
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["month","unix_timestamp"]
tourism
0.1
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["month","unix_timestamp"]
12
43
TrainingMethod.TEACHER_FORCING
19201650
17518.19531
153879840
100
2185.97974
0.22503
1.66854
86862520
0.21684
2843.33545
0.20424
19.88151
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garethmd
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[]
tourism
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tourism_monthly_dataset.tsf
M
40
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[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["feat_scale"]
12
46
TrainingMethod.TEACHER_FORCING
18289168
17749.88672
155915008
100
2082.09985
0.22896
1.65108
68624208
0.21544
2664.24341
0.20495
20.37052
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garethmd
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["month"]
tourism
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["month","feat_scale"]
12
44
TrainingMethod.TEACHER_FORCING
19340640
17491.32031
153643760
100
2201.80347
0.22487
1.67984
88819400
0.21816
2840.0332
0.20513
18.30235
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garethmd
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32
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["month","unix_timestamp"]
tourism
0.1
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["month","unix_timestamp"]
12
44
TrainingMethod.TEACHER_FORCING
19658624
17414.44336
152968464
100
2238.00366
0.23039
1.68436
94195824
0.22041
2883.54346
0.20665
18.8226
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garethmd
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32
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["unix_timestamp"]
tourism
0.1
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["unix_timestamp","feat_scale"]
12
44
TrainingMethod.TEACHER_FORCING
19824206
17372.56641
152600608
100
2256.85425
0.22907
1.706
98044552
0.22268
2887.10889
0.20702
18.3665
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garethmd
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["unix_timestamp"]
12
44
TrainingMethod.TEACHER_FORCING
19853736
17391.82031
152769728
100
2260.21582
0.22991
1.68337
95899024
0.21748
2899.90601
0.20745
20.42
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garethmd
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[]
tourism
0.1
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["feat_scale"]
12
45
TrainingMethod.TEACHER_FORCING
19880194
17446.18555
153247296
100
2263.22803
0.22855
1.70987
99855936
0.22202
2899.88892
0.20747
18.43754
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garethmd
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["unix_timestamp","feat_scale"]
12
43
TrainingMethod.TEACHER_FORCING
18821828
17734.14258
155776704
100
2142.73999
0.22949
1.7034
74000632
0.22498
2761.33667
0.2075
20.11585
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garethmd
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tourism
0.1
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["unix_timestamp"]
12
46
TrainingMethod.TEACHER_FORCING
19660428
17398.29297
152826608
100
2238.20898
0.23109
1.68893
88387864
0.21953
2880.84155
0.20787
19.9899
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garethmd
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["month"]
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["month"]
12
44
TrainingMethod.TEACHER_FORCING
19853702
17394.38867
152792304
100
2260.21191
0.23153
1.7166
90562760
0.22197
2903.61963
0.20814
20.44736
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garethmd
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tourism_monthly_dataset.tsf
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40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["month","unix_timestamp"]
12
46
TrainingMethod.TEACHER_FORCING
19360962
17584.56055
154462784
100
2204.1167
0.23036
1.70252
74111304
0.21958
2824.33936
0.20885
18.09924
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garethmd
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tourism
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tourism_monthly_dataset.tsf
M
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[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
[]
12
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TrainingMethod.TEACHER_FORCING
18917988
17539.09766
154063456
100
2153.68726
0.23481
1.69489
75727648
0.21926
2756.91528
0.20905
20.70911
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garethmd
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["month","unix_timestamp"]
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["month","unix_timestamp","feat_scale"]
12
42
TrainingMethod.TEACHER_FORCING
20633172
17299.36328
151957616
100
2348.94946
0.23374
1.71913
105829256
0.22566
3003.24072
0.20928
16.13943
Finished
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garethmd
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32
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["month","unix_timestamp"]
tourism
0.1
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["month","unix_timestamp","feat_scale"]
12
42
TrainingMethod.TEACHER_FORCING
20633172
17299.36328
151957616
100
2348.94946
0.23374
1.71913
105829256
0.22566
3003.24072
0.20928
16.13943
Finished
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garethmd
15
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32
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["unix_timestamp"]
tourism
0.1
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
["unix_timestamp","feat_scale"]
12
42
TrainingMethod.TEACHER_FORCING
19961510
17423.41211
153047248
100
2272.4856
0.23527
1.70353
95663536
0.22625
2906.42627
0.20992
17.96406
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garethmd
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[]
tourism
0.1
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tourism_monthly_dataset.tsf
M
40
1
[1,2,3,4,5,6,10,11,12,22,23,24,34,35,36]
0.001
2
1
10
24
lstm
[]
12
43
TrainingMethod.TEACHER_FORCING
20571064
17266.94531
151672864
100
2341.87891
0.23518
1.71596
109566400
0.22397
3002.48193
0.21007
19.4015
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