Initial version of Seasonal Naive

cli/conf/seasonal_naive_eval/data/etth1_test.yaml

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+_target_: uni2ts.eval_util.data.get_custom_eval_dataset
+dataset_name: ETTh1_eval
+offset: 14400
+windows: 2785
+distance: 1
+prediction_length: 96
+mode: null

cli/conf/seasonal_naive_eval/data/etth1_val.yaml

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+_target_: uni2ts.eval_util.data.get_custom_eval_dataset
+dataset_name: ETTh1_eval
+offset: 11520
+windows: 2785
+distance: 1
+prediction_length: 96
+mode: null

cli/conf/seasonal_naive_eval/data/gluonts_test.yaml

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+_target_: uni2ts.eval_util.data.get_gluonts_test_dataset
+dataset_name: ???
+prediction_length: null
+mode: S

cli/conf/seasonal_naive_eval/data/gluonts_val.yaml

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+_target_: uni2ts.eval_util.data.get_gluonts_val_dataset
+dataset_name: ???
+prediction_length: null
+mode: S

cli/conf/seasonal_naive_eval/data/lsf_test.yaml

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+_target_: uni2ts.eval_util.data.get_lsf_test_dataset
+dataset_name: ???
+prediction_length: ???
+mode: S

cli/conf/seasonal_naive_eval/data/lsf_val.yaml

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+_target_: uni2ts.eval_util.data.get_lsf_val_dataset
+dataset_name: ???
+prediction_length: ???
+mode: S

cli/conf/seasonal_naive_eval/data/monash.yaml

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+_target_: uni2ts.eval_util.data.get_gluonts_test_dataset
+dataset_name: ???
+prediction_length: null
+mode: S

cli/conf/seasonal_naive_eval/default.yaml

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+hydra:
+  run:
+    dir: outputs/eval/${hydra:runtime.choices.model}/${exp_name}/${data.dataset_name}/${data.mode}/cl${model.context_length}_pl${data.prediction_length}
+defaults:
+  - model: ???
+  - data: ???
+  - _self_
+exp_name: ???
+metrics:
+  - _target_: gluonts.ev.metrics.MSE
+  - _target_: uni2ts.eval_util.metrics.MedianMSE
+  - _target_: gluonts.ev.metrics.MAE
+  - _target_: gluonts.ev.metrics.MASE
+  - _target_: gluonts.ev.metrics.MAPE
+  - _target_: gluonts.ev.metrics.SMAPE
+  - _target_: gluonts.ev.metrics.MSIS
+  - _target_: gluonts.ev.metrics.RMSE
+  - _target_: gluonts.ev.metrics.NRMSE
+  - _target_: gluonts.ev.metrics.ND
+  - _target_: gluonts.ev.metrics.MeanWeightedSumQuantileLoss
+    quantile_levels: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
+batch_size: 512
+min_batch_size: 16
+device: auto

cli/conf/seasonal_naive_eval/model/moirai_lightning_ckpt.yaml

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+_target_: uni2ts.model.seasonal_naive_moirai.MoiraiForecast.load_from_checkpoint
+checkpoint_path: ...
+pretrained_checkpoint_path: null
+num_samples: 100
+patch_size: ???
+context_length: ???

cli/conf/seasonal_naive_finetune/data/electricity.yaml

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+_target_: uni2ts.data.builder.simple.SimpleDatasetBuilder
+dataset: electricity

cli/conf/seasonal_naive_finetune/data/etth1.yaml

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+_target_: uni2ts.data.builder.simple.SimpleDatasetBuilder
+dataset: ETTh1
+weight: 1000

cli/conf/seasonal_naive_finetune/data/etth2.yaml

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+_target_: uni2ts.data.builder.simple.SimpleDatasetBuilder
+dataset: ETTh2

cli/conf/seasonal_naive_finetune/data/ettm1.yaml

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+_target_: uni2ts.data.builder.simple.SimpleDatasetBuilder
+dataset: ETTm1

cli/conf/seasonal_naive_finetune/data/ettm2.yaml

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+_target_: uni2ts.data.builder.simple.SimpleDatasetBuilder
+dataset: ETTm2

cli/conf/seasonal_naive_finetune/data/weather.yaml

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+_target_: uni2ts.data.builder.simple.SimpleDatasetBuilder
+dataset: weather

cli/conf/seasonal_naive_finetune/default.yaml

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+hydra:
+  run:
+    dir: outputs/sn_finetune/${hydra:runtime.choices.model}/${exp_name}/${model.finetune_pattern}/${hydra:runtime.choices.data}/${run_name}
+defaults:
+  - model: ???
+  - data: ???
+  - val_data: null
+  - _self_
+exp_name: ???
+run_name: ???
+seed: 0
+tf32: true
+compile: false  # set to mode: default, reduce-overhead, max-autotune
+ckpt_path: null
+trainer:
+  _target_: lightning.Trainer
+  accelerator: auto
+  strategy: auto
+  devices: auto
+  num_nodes: 1
+  precision: 32
+  logger:
+      _target_: lightning.pytorch.loggers.TensorBoardLogger
+      save_dir: ${hydra:runtime.output_dir}
+      name: logs
+  callbacks:
+    - _target_: lightning.pytorch.callbacks.LearningRateMonitor
+      logging_interval: epoch
+    - _target_: lightning.pytorch.callbacks.ModelCheckpoint
+      dirpath: ${hydra:runtime.output_dir}/checkpoints
+      monitor: val/PackedNLLLoss
+      save_weights_only: true
+      mode: min
+      save_top_k: 1
+      every_n_epochs: 1
+    - _target_: lightning.pytorch.callbacks.EarlyStopping
+      monitor: val/PackedNLLLoss
+      min_delta: 0.0
+      patience: 30
+      mode: min
+      strict: false
+      verbose: true
+  max_epochs: 1000
+  enable_progress_bar: true
+  accumulate_grad_batches: 1
+  gradient_clip_val: 1.0
+  gradient_clip_algorithm: norm
+train_dataloader:
+  _target_: uni2ts.data.loader.DataLoader
+  batch_size: 32
+  batch_size_factor: 2.0
+  cycle: true
+  num_batches_per_epoch: 10
+  shuffle: true
+  num_workers: 11
+  collate_fn:
+    _target_: uni2ts.data.loader.PackCollate
+    max_length: ${model.module_kwargs.max_seq_len}
+    seq_fields: ${cls_getattr:${model._target_},seq_fields}
+    pad_func_map: ${cls_getattr:${model._target_},pad_func_map}
+  pin_memory: true
+  drop_last: false
+  fill_last: false
+  worker_init_fn: null
+  prefetch_factor: 2
+  persistent_workers: true
+val_dataloader:
+  _target_: uni2ts.data.loader.DataLoader
+  batch_size: 32
+  batch_size_factor: 2.0
+  cycle: false
+  num_batches_per_epoch: null
+  shuffle: false
+  num_workers: 11
+  collate_fn:
+    _target_: uni2ts.data.loader.PackCollate
+    max_length: ${model.module_kwargs.max_seq_len}
+    seq_fields: ${cls_getattr:${model._target_},seq_fields}
+    pad_func_map: ${cls_getattr:${model._target_},pad_func_map}
+  pin_memory: false
+  drop_last: false
+  fill_last: true
+  worker_init_fn: null
+  prefetch_factor: 2
+  persistent_workers: true

cli/conf/seasonal_naive_finetune/model/moirai_1.1_R_small.yaml

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+# load a pretrained checkpoint from huggingface hub
+_target_: uni2ts.model.seasonal_naive_moirai.MoiraiFinetune
+module:
+  _target_: uni2ts.model.seasonal_naive_moirai.MoiraiModule.from_pretrained
+  pretrained_model_name_or_path: Salesforce/moirai-1.1-R-small
+module_kwargs:
+  _target_: builtins.dict
+  distr_output:
+    _target_: uni2ts.distribution.MixtureOutput
+    components:
+      - _target_: uni2ts.distribution.StudentTOutput
+      - _target_: uni2ts.distribution.NormalFixedScaleOutput
+      - _target_: uni2ts.distribution.NegativeBinomialOutput
+      - _target_: uni2ts.distribution.LogNormalOutput
+  d_model: 384
+  num_layers: 6
+  patch_sizes: ${as_tuple:[8, 16, 32, 64, 128]}
+  max_seq_len: 512
+  attn_dropout_p: 0.0
+  dropout_p: 0.0
+  scaling: true
+min_patches: 2
+min_mask_ratio: 0.15
+max_mask_ratio: 0.5
+max_dim: 128
+loss_func:
+  _target_: uni2ts.loss.packed.PackedNLLLoss
+val_metric:
+  - _target_: uni2ts.loss.packed.PackedMSELoss
+  - _target_: uni2ts.loss.packed.PackedNRMSELoss
+    normalize: absolute_target_squared
+lr: 1e-5
+weight_decay: 1e-1
+beta1: 0.9
+beta2: 0.98
+num_training_steps: ${mul:${trainer.max_epochs},${train_dataloader.num_batches_per_epoch}}
+num_warmup_steps: 0
+patch_size: null
+context_length: null
+prediction_length: null
+finetune_pattern: full
+replace_distr_output: False
+apply_seasonal_naive: True

cli/conf/seasonal_naive_finetune/val_data/electricity.yaml

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+_target_: uni2ts.data.builder.ConcatDatasetBuilder
+_args_:
+  _target_: uni2ts.data.builder.simple.generate_eval_builders
+  dataset: electricity_eval
+  offset: 18412 # Same as _lsf_dataset.py
+  eval_length: 2630 # Same as _lsf_dataset.py, test_length=5260
+  prediction_lengths: ???
+  context_lengths: ???
+  patch_sizes: ???
+
+#  prediction_lengths: [96, 192, 336, 720]
+#  context_lengths: [3000]
+#  patch_sizes: [32, 64]  # freq='h'

cli/conf/seasonal_naive_finetune/val_data/etth1.yaml

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+_target_: uni2ts.data.builder.ConcatDatasetBuilder
+_args_:
+  _target_: uni2ts.data.builder.simple.generate_eval_builders
+  dataset: ETTh1_eval
+  offset: 11520
+  eval_length: 2880
+  prediction_lengths: [96, 192, 336, 720]
+  context_lengths: [1000, 2000, 3000, 4000, 5000]
+  patch_sizes: [32, 64]

cli/conf/seasonal_naive_finetune/val_data/etth1_multi.yaml

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+- _target_: uni2ts.data.builder.simple.SimpleEvalDatasetBuilder
+  dataset: ETTh1_eval
+  offset: 11520
+  windows: 10
+  distance: 96
+  prediction_length: 96
+  context_length: 1000
+  patch_size: 32
+- _target_: uni2ts.data.builder.simple.SimpleEvalDatasetBuilder
+  dataset: ETTh1_eval
+  offset: 11520
+  windows: 10
+  distance: 192
+  prediction_length: 192
+  context_length: 1000
+  patch_size: 32

cli/conf/seasonal_naive_finetune/val_data/etth2.yaml

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+_target_: uni2ts.data.builder.ConcatDatasetBuilder
+_args_:
+  _target_: uni2ts.data.builder.simple.generate_eval_builders
+  dataset: ETTh2_eval
+  offset: 8640  # Same as _lsf_dataset.py
+  eval_length: 2880  # Same as _lsf_dataset.py
+  prediction_lengths: ???
+  context_lengths: ???
+  patch_sizes: ???
+
+#  prediction_lengths: [ 96, 192, 336, 720 ]
+#  context_lengths: [ 3000 ]
+#  patch_sizes: [ 32, 64 ]

cli/conf/seasonal_naive_finetune/val_data/ettm1.yaml

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+_target_: uni2ts.data.builder.ConcatDatasetBuilder
+_args_:
+  _target_: uni2ts.data.builder.simple.generate_eval_builders
+  dataset: ETTm1_eval
+  offset: 34560  # Same as _lsf_dataset.py
+  eval_length: 11520  # Same as _lsf_dataset.py
+  prediction_lengths: ???
+  context_lengths: ???
+  patch_sizes: ???
+
+
+#  prediction_lengths: [96, 192, 336, 720]
+#  context_lengths: [ 3000 ]
+#  patch_sizes: [ 32, 64, 128 ]  # freq="15T"

cli/conf/seasonal_naive_finetune/val_data/ettm2.yaml

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+_target_: uni2ts.data.builder.ConcatDatasetBuilder
+_args_:
+  _target_: uni2ts.data.builder.simple.generate_eval_builders
+  dataset: ETTm2_eval
+  offset: 34560  # Same as _lsf_dataset.py
+  eval_length: 11520  # Same as _lsf_dataset.py
+  prediction_lengths: ???
+  context_lengths: ???
+  patch_sizes: ???
+
+
+#  prediction_lengths: [96, 192, 336, 720]
+#  context_lengths: [3000]
+#  patch_sizes: [32, 64, 128]  # "freq=15T"

cli/conf/seasonal_naive_finetune/val_data/weather.yaml

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+_target_: uni2ts.data.builder.ConcatDatasetBuilder
+_args_:
+  _target_: uni2ts.data.builder.simple.generate_eval_builders
+  dataset: weather_eval
+  offset: 36887 # Same as _lsf_dataset.py
+  eval_length: 5269 # Same as _lsf_dataset.py; test_length=10539
+  prediction_lengths: ???
+  context_lengths: ???
+  patch_sizes: ???

src/uni2ts/model/seasonal_naive_moirai/__init__.py

@@ -0,0 +1,25 @@
+#  Copyright (c) 2024, Salesforce, Inc.
+#  SPDX-License-Identifier: Apache-2
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+from .finetune import MoiraiFinetune
+from .forecast import MoiraiForecast
+from .module import MoiraiModule
+
+
+__all__ = [
+    "MoiraiFinetune",
+    "MoiraiForecast",
+    "MoiraiModule",
+]