[AINode]: Integrate MOMENT as a builtin forecasting model

[Eliaaazzz]

Description

Integrate MOMENT Time Series Foundation Model

Integrate MOMENT (A Family of Open Time-series Foundation Models, ICML 2024, Auton Lab/CMU) into IoTDB AINode as a built-in forecasting model.

MOMENT uses a T5 encoder-only backbone with patch-based input embedding and RevIN (Reversible Instance Normalization). It supports zero-shot forecasting with a fixed input length of 512 timesteps
and channel-independent multivariate processing.

Architecture:
Input [batch, n_channels, 512]
→ RevIN normalization
→ Patching (patch_len=8, stride=8 → 64 patches)
→ Patch embedding (linear → d_model)
→ T5 Encoder (self-attention)
→ ForecastingHead (linear → forecast_horizon)
→ RevIN denormalization
→ Output [batch, n_channels, forecast_horizon]

The default variant is MOMENT-1-small (~152 MB, d_model=512, 6 layers). Weights are downloaded from HuggingFace Hub on first use, consistent with existing built-in models (Sundial, Timer-XL,
Chronos2, etc.).

Design Decisions

• Vendor the model code rather than depending on the external momentfm pip package, following the same pattern as Chronos2 ([AINode] Integrate Chronos2 as builtin forecasting model #16903) and Toto ([AINode]: Integrate toto as a builtin forecasting model #17322). This avoids an additional runtime
  dependency and gives full control over the loading process.
• Wrap as PreTrainedModel with custom from_pretrained() to handle upstream MOMENT's flat state-dict key mapping (prefixing with moment.*) and T5 config extraction from config.json.
• Use MOMENT-1-small as the default built-in variant to minimize download size and memory usage while still providing useful forecasting capability.
• Iterative long-horizon forecasting in the pipeline: for output_length > model_forecast_horizon, predictions are appended to the context window and fed back as input, similar to
  autoregressive decoding.
• Fixed 512-length input handling: inputs shorter than 512 are left-padded with zeros and masked; inputs longer than 512 are truncated to the last 512 timesteps.

Bug Fix

Fixed unquoted $JAVA variable in confignode-env.sh and datanode-env.sh to handle paths with spaces (e.g. C:\Program Files\...).

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This PR has:

• been self-reviewed.
• added or updated version, license, or notice information
• added comments explaining the "why" and the intent of the code wherever would not be obvious for an unfamiliar reader.
• added integration tests.
• been tested in a test IoTDB cluster.

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Key changed/added classes (or packages if there are too many classes) in this PR

• iotdb-core/ainode/iotdb/ainode/core/model/moment/ — New MOMENT model package
  • configuration_moment.py — MomentConfig(PretrainedConfig)
  • modeling_moment.py — MomentForPrediction(PreTrainedModel) with RevIN, Patching, T5 Encoder, ForecastingHead
  • pipeline_moment.py — MomentPipeline(ForecastPipeline) with padding/truncation and iterative forecasting
• iotdb-core/ainode/iotdb/ainode/core/model/model_info.py — Added moment to BUILTIN_HF_TRANSFORMERS_MODEL_MAP
• iotdb-core/ainode/iotdb/ainode/core/constant.py — Added memory estimate for MOMENT
• integration-test/.../AINodeTestUtils.java — Added moment to BUILTIN_LTSM_MAP
• LICENSE — Added MIT attribution for MOMENT source code
• scripts/conf/confignode-env.sh, scripts/conf/datanode-env.sh — Fixed unquoted $JAVA variable