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AMOS team contributions: forecasting, anomaly detection, decomposition, visualization #950

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35eccce
Commit all AMOS changes to upstream repo for PR
simonselbig Jan 25, 2026
18dd377
remove duplicate anomaly detection file
simonselbig Jan 25, 2026
8c6a601
last changes to mkdocs & environment
simonselbig Jan 26, 2026
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1 change: 1 addition & 0 deletions ...ce/pipelines/anomaly_detection/spark/iqr/decomposition_iqr_anomaly_detection.md
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::: src.sdk.python.rtdip_sdk.pipelines.anomaly_detection.spark.iqr.decomposition_iqr_anomaly_detection
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::: src.sdk.python.rtdip_sdk.pipelines.anomaly_detection.spark.iqr.iqr_anomaly_detection
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::: src.sdk.python.rtdip_sdk.pipelines.anomaly_detection.spark.mad.mad_anomaly_detection
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::: src.sdk.python.rtdip_sdk.pipelines.data_quality.data_manipulation.spark.select_columns_by_correlation
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::: src.sdk.python.rtdip_sdk.pipelines.decomposition.pandas.classical_decomposition
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Expand Up @@ -73,6 +73,15 @@ dependencies:
- statsmodels>=0.14.1,<0.15.0
- pmdarima>=2.0.4
- scikit-learn>=1.3.0,<1.6.0
# ML/Forecasting dependencies added by AMOS team
- tensorflow>=2.18.0,<3.0.0
- tf-keras>=2.15,<2.19
- xgboost>=2.0.0,<3.0.0
- plotly>=5.0.0
- python-kaleido>=0.2.0
- prophet==1.2.1
- sktime==0.40.1
- catboost==1.2.8
- pip:
# protobuf installed via pip to avoid libabseil conflicts with conda libarrow
- protobuf>=5.29.0,<5.30.0
Expand All @@ -92,3 +101,5 @@ dependencies:
- eth-typing>=5.0.1,<6.0.0
- pandas>=2.0.1,<2.3.0
- moto[s3]>=5.0.16,<6.0.0
# AutoGluon for time series forecasting (AMOS team)
- autogluon.timeseries>=1.1.1,<2.0.0
89 changes: 74 additions & 15 deletions mkdocs.yml
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Expand Up @@ -172,6 +172,7 @@ nav:
- Delta Sharing: sdk/code-reference/pipelines/sources/python/delta_sharing.md
- ENTSO-E: sdk/code-reference/pipelines/sources/python/entsoe.md
- MFFBAS: sdk/code-reference/pipelines/sources/python/mffbas.md
- Azure Blob: sdk/code-reference/pipelines/sources/python/azure_blob.md
- Transformers:
- Spark:
- Binary To String: sdk/code-reference/pipelines/transformers/spark/binary_to_string.md
Expand Down Expand Up @@ -245,27 +246,85 @@ nav:
- Interval Based: sdk/code-reference/pipelines/data_quality/monitoring/spark/identify_missing_data_interval.md
- Pattern Based: sdk/code-reference/pipelines/data_quality/monitoring/spark/identify_missing_data_pattern.md
- Moving Average: sdk/code-reference/pipelines/data_quality/monitoring/spark/moving_average.md
- Data Manipulation:
- Duplicate Detetection: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/duplicate_detection.md
- Out of Range Value Filter: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/out_of_range_value_filter.md
- Flatline Filter: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/flatline_filter.md
- Gaussian Smoothing: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/gaussian_smoothing.md
- Dimensionality Reduction: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/dimensionality_reduction.md
- Interval Filtering: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/interval_filtering.md
- K-Sigma Anomaly Detection: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/k_sigma_anomaly_detection.md
- Missing Value Imputation: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/missing_value_imputation.md
- Normalization:
- Normalization: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/normalization/normalization.md
- Normalization Mean: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/normalization/normalization_mean.md
- Normalization MinMax: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/normalization/normalization_minmax.md
- Normalization ZScore: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/normalization/normalization_zscore.md
- Denormalization: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/normalization/denormalization.md
- Data Manipulation:
- Spark:
- Duplicate Detetection: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/duplicate_detection.md
- Out of Range Value Filter: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/out_of_range_value_filter.md
- Flatline Filter: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/flatline_filter.md
- Gaussian Smoothing: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/gaussian_smoothing.md
- Dimensionality Reduction: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/dimensionality_reduction.md
- Interval Filtering: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/interval_filtering.md
- K-Sigma Anomaly Detection: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/k_sigma_anomaly_detection.md
- Missing Value Imputation: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/missing_value_imputation.md
- Chronological Sort: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/chronological_sort.md
- Cyclical Encoding: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/cyclical_encoding.md
- Datetime Features: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/datetime_features.md
- Datetime String Conversion: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/datetime_string_conversion.md
- Lag Features: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/lag_features.md
- MAD Outlier Detection: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/mad_outlier_detection.md
- Mixed Type Separation: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/mixed_type_separation.md
- Rolling Statistics: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/rolling_statistics.md
- Drop Empty Columns: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/drop_empty_columns.md
- Drop Columns by NaN Percentage: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/drop_columns_by_nan_percentage.md
- Select Columns by Correlation: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/select_columns_by_correlation.md
- Normalization:
- Normalization: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/normalization/normalization.md
- Normalization Mean: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/normalization/normalization_mean.md
- Normalization MinMax: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/normalization/normalization_minmax.md
- Normalization ZScore: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/normalization/normalization_zscore.md
- Denormalization: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/normalization/denormalization.md
- Pandas:
- Chronological Sort: sdk/code-reference/pipelines/data_quality/data_manipulation/pandas/chronological_sort.md
- Cyclical Encoding: sdk/code-reference/pipelines/data_quality/data_manipulation/pandas/cyclical_encoding.md
- Datetime Features: sdk/code-reference/pipelines/data_quality/data_manipulation/pandas/datetime_features.md
- Datetime String Conversion: sdk/code-reference/pipelines/data_quality/data_manipulation/pandas/datetime_string_conversion.md
- Lag Features: sdk/code-reference/pipelines/data_quality/data_manipulation/pandas/lag_features.md
- MAD Outlier Detection: sdk/code-reference/pipelines/data_quality/data_manipulation/pandas/mad_outlier_detection.md
- Mixed Type Separation: sdk/code-reference/pipelines/data_quality/data_manipulation/pandas/mixed_type_separation.md
- One-Hot Encoding: sdk/code-reference/pipelines/data_quality/data_manipulation/pandas/one_hot_encoding.md
- Rolling Statistics: sdk/code-reference/pipelines/data_quality/data_manipulation/pandas/rolling_statistics.md
- Drop Empty Columns: sdk/code-reference/pipelines/data_quality/data_manipulation/pandas/drop_empty_columns.md
- Drop Columns by NaN Percentage: sdk/code-reference/pipelines/data_quality/data_manipulation/pandas/drop_columns_by_nan_percentage.md
- Select Columns by Correlation: sdk/code-reference/pipelines/data_quality/data_manipulation/pandas/select_columns_by_correlation.md
- Forecasting:
- Data Binning: sdk/code-reference/pipelines/forecasting/spark/data_binning.md
- Linear Regression: sdk/code-reference/pipelines/forecasting/spark/linear_regression.md
- Arima: sdk/code-reference/pipelines/forecasting/spark/arima.md
- Auto Arima: sdk/code-reference/pipelines/forecasting/spark/auto_arima.md
- K Nearest Neighbors: sdk/code-reference/pipelines/forecasting/spark/k_nearest_neighbors.md
- Prophet: sdk/code-reference/pipelines/forecasting/spark/prophet.md
- LSTM TimeSeries: sdk/code-reference/pipelines/forecasting/spark/lstm_timeseries.md
- XGBoost TimeSeries: sdk/code-reference/pipelines/forecasting/spark/xgboost_timeseries.md
- CatBoost TimeSeries: sdk/code-reference/pipelines/forecasting/spark/catboost_timeseries.md
- AutoGluon TimeSeries: sdk/code-reference/pipelines/forecasting/spark/autogluon_timeseries.md
- Prediction Evaluation: sdk/code-reference/pipelines/forecasting/prediction_evaluation.md
- Decomposition:
- Pandas:
- Classical Decomposition: sdk/code-reference/pipelines/decomposition/pandas/classical_decomposition.md
- STL Decomposition: sdk/code-reference/pipelines/decomposition/pandas/stl_decomposition.md
- MSTL Decomposition: sdk/code-reference/pipelines/decomposition/pandas/mstl_decomposition.md
- Spark:
- Classical Decomposition: sdk/code-reference/pipelines/decomposition/spark/classical_decomposition.md
- STL Decomposition: sdk/code-reference/pipelines/decomposition/spark/stl_decomposition.md
- MSTL Decomposition: sdk/code-reference/pipelines/decomposition/spark/mstl_decomposition.md
- Anomaly Detection:
- Spark:
- IQR:
- IQR Anomaly Detection: sdk/code-reference/pipelines/anomaly_detection/spark/iqr/iqr_anomaly_detection.md
- Decomposition IQR Anomaly Detection: sdk/code-reference/pipelines/anomaly_detection/spark/iqr/decomposition_iqr_anomaly_detection.md
- MAD:
- MAD Anomaly Detection: sdk/code-reference/pipelines/anomaly_detection/spark/mad/mad_anomaly_detection.md
- Visualization:
- Matplotlib:
- Anomaly Detection: sdk/code-reference/pipelines/visualization/matplotlib/anomaly_detection.md
- Model Comparison: sdk/code-reference/pipelines/visualization/matplotlib/comparison.md
- Decomposition: sdk/code-reference/pipelines/visualization/matplotlib/decomposition.md
- Forecasting: sdk/code-reference/pipelines/visualization/matplotlib/forecasting.md
- Plotly:
- Anomaly Detection: sdk/code-reference/pipelines/visualization/plotly/anomaly_detection.md
- Model Comparison: sdk/code-reference/pipelines/visualization/plotly/comparison.md
- Decomposition: sdk/code-reference/pipelines/visualization/plotly/decomposition.md
- Forecasting: sdk/code-reference/pipelines/visualization/plotly/forecasting.md

- Jobs: sdk/pipelines/jobs.md
- Deploy:
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13 changes: 13 additions & 0 deletions src/sdk/python/rtdip_sdk/pipelines/anomaly_detection/__init__.py
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# Copyright 2025 RTDIP
#
# 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.
29 changes: 29 additions & 0 deletions src/sdk/python/rtdip_sdk/pipelines/anomaly_detection/interfaces.py
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# Copyright 2025 RTDIP
#
# 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 abc import abstractmethod

from great_expectations.compatibility.pyspark import DataFrame

from ..interfaces import PipelineComponentBaseInterface


class AnomalyDetectionInterface(PipelineComponentBaseInterface):

@abstractmethod
def __init__(self):
pass

@abstractmethod
def detect(self, df: DataFrame) -> DataFrame:
pass
13 changes: 13 additions & 0 deletions src/sdk/python/rtdip_sdk/pipelines/anomaly_detection/spark/__init__.py
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# Copyright 2025 RTDIP
#
# 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.
9 changes: 9 additions & 0 deletions src/sdk/python/rtdip_sdk/pipelines/anomaly_detection/spark/iqr/__init__.py
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from .iqr_anomaly_detection import IQRAnomalyDetectionComponent
from .decomposition_iqr_anomaly_detection import (
DecompositionIQRAnomalyDetectionComponent,
)

__all__ = [
"IQRAnomalyDetectionComponent",
"DecompositionIQRAnomalyDetectionComponent",
]
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