AutoML Industrial Time Series Forecasting 2026
AutoML Industrial Time Series Forecasting
AutoML for industrial time series forecasting automates end-to-end ML pipelines — from sensor ingestion to drift-aware deployment. This dataset covers 60+ patent filings and literature records spanning 2016–2026.
Automating the Industrial Forecasting Pipeline
AutoML for industrial time series forecasting automates the complete ML pipeline — from raw sensor data ingestion and feature engineering through model selection, hyperparameter tuning, and deployment — applied specifically to temporally ordered data from industrial systems. The field must address non-stationarity, irregular sampling, concept drift, multi-step prediction horizons, and real-time operational constraints.
The technology bridges traditional statistical methods such as ARIMA and exponential smoothing with modern deep learning architectures including LSTM, GRU, and Transformer models, all managed under an automation wrapper that handles pipeline lifecycle. Core sub-domains include pipeline automation, temporal feature engineering, ensemble and meta-learning, and edge-cloud integrated deployment.
Publication dates among retrieved records span 2016 to 2026, revealing a three-phase evolution: infrastructure building and problem framing (2016–2019), core patent development around pipeline generation and cloud ensemble methods (2020–2022), and a shift toward production hardening and edge-cloud architectures (2023–2026). The 2025–2026 filings consistently emphasize operational autonomy through continuous retraining and drift detection.
In this dataset, IBM, ServiceNow, Oracle, Microsoft, and HPE collectively account for a disproportionate share of production-ready AutoML pipeline patents in retrieved records. A long tail of individual inventors and smaller entities — including Mastercard IN, Arti Analytics, and UAB Rivile — indicates distributed participation in application-specific niches.
Filing Trends and Technology Cluster Distribution
Analysis of retrieved records reveals clear concentration in pipeline automation and cloud/IT application domains, with emerging clusters in edge deployment and transformer-based architectures appearing predominantly in 2025–2026 filings.
Patent Filings by Technology Cluster (Dataset Snapshot)
Pipeline automation and cloud/IT operations together represent the largest share of filings in this dataset, reflecting early commercial deployments by IBM, ServiceNow, Oracle, and Microsoft.
↗ Click bars to exploreFiling Activity by Phase — Retrieved Records (2016–2026)
Filing activity in this dataset is concentrated in the 2020–2022 core development phase, with a second surge in 2025–2026 driven by edge deployment and transformer architecture patents.
↗ Click bars to exploreKey Industrial Deployment Domains for AutoML Time Series Forecasting
Retrieved records span five primary application domains where automated time series forecasting has been deployed or patented: industrial IoT and predictive maintenance, energy and smart grid, cloud infrastructure and IT operations, supply chain and manufacturing demand, and financial services and healthcare.
Industrial IoT and Smart Buildings
Fujitsu’s 2026 EP/US filings define an AutoML workflow ingesting sensor data from built environments, using checkpoint-based triggers to retrain models when new records exceed a threshold. GE Infrastructure Technology’s 2025 WO patent deploys automated feature engineering for sensor, actuator, and control monitoring nodes targeting anomaly and fault detection. Literature benchmarks multivariate forecasting on gas turbine sensor datasets (2020).
Industrial IoTEnergy and Smart Grid Operations
Load forecasting patents target smart grid operations (Nitin Tanwar, 2020, IN/WO). Literature benchmarks SARIMA, Prophet, XGBoost, and LSTM on wastewater treatment plant flow data, and ensemble voting regressors combining Extra Trees, Random Forest, LightGBM, Gradient Boosting, and KNN on wind farm generation. The ProLoaF tool and auto.arima/Facebook Prophet are benchmarked for power system wholesale market applications (2022).
Energy ForecastingCloud Infrastructure and IT Ops
ServiceNow holds four active US patents applying time series ML to IT resource scheduling — predicting when users will request operations to pre-schedule automations for efficient resource utilization (2021–2022). Microsoft’s ensemble system trains multiple models on VM CPU, disk, and network usage metrics, selecting the best-performing for production prediction (2020–2022 US/WO). EMC’s 2016 virtual machine capacity planning patent represents the earliest ML-driven forecasting for IT infrastructure in this dataset.
Cloud IT OperationsSupply Chain and Manufacturing Demand
Home Depot’s 2024 US patent on divergent-scale time series modeling targets demand forecasting across thousands of geographically diverse product-location combinations. Kinaxis holds active US patents for ML-based supply chain design correction incorporating lead time, weather, and economic indicator data (2020–2021). Literature benchmarks 21 algorithms on automotive OEM erratic demand patterns, finding global ML models superior to local models.
Supply Chain AnalyticsKey Patent Assignees in AutoML Time Series Forecasting (Retrieved Records)
In this dataset, IBM and ServiceNow each account for 4 filings in retrieved records, representing the highest individual filing volumes. Oracle, Microsoft, and Dell each contribute 3 filings in retrieved records, with IBM’s filings concentrated on pipeline generation and ranking infrastructure.
Top Assignees by Filing Count — AutoML Time Series (Dataset Snapshot)
↗ Click bars to exploreInternational Business Machines Corporation
IBM accounts for 4 filings in this dataset spanning 2022–2025 across US and WO jurisdictions. Core patents cover automated time series forecasting pipeline generation (2024, US) and pipeline ranking via projected learning curves (2022, WO), plus a skills and tasks demand forecasting patent targeting AI hardware accelerator demand prediction (2025, US). The pipeline generation and ranking patents are active in the US.
United StatesServiceNow, Inc.
ServiceNow holds 4 active US filings in this dataset, all under the title “Time series data analysis,” filed across 2021–2022. These patents apply time series ML to IT resource scheduling — predicting when users will request operations to pre-schedule automations for efficient resource utilization. All 4 filings are active US patents concentrated in the cloud IT operations application domain.
United StatesFive Forward-Looking Vectors in AutoML Time Series (2025–2026)
The most recent filings in this dataset (2025–2026) reveal five forward-looking architectural and operational directions, all emphasizing production autonomy, edge deployment, and modular reusability of ML components.
Edge-Embedded AutoML with Automated Drift Response
Hewlett Packard Enterprise’s dual US/DE filings (2025) describe architectures where edge computing resources both serve forecasts and detect prediction drift, triggering centralized cloud retraining autonomously. This eliminates manual model monitoring — a critical operational gap in prior systems. The pattern also appears in Fujitsu’s checkpoint-based retraining trigger mechanism filed in 2026 across EP and US jurisdictions.
Transformer Architectures for Multivariate Industrial Sensor Streams
Symbiosis International’s 2025 IN patent applies patch segmentation and instance normalization — techniques borrowed from vision Transformers — to industrial sensor time series for predictive maintenance with natural language interpretability. The patent targets multivariate sensor streams including temperature, pressure, vibration, and speed. A 2023 literature review confirms Transformers and Graph Neural Networks are the most active deep learning architecture research areas for time series forecasting.
IBM Pipeline Automation vs. Oracle One-Pass AutoML: Architectural Contrast
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| Dimension | IBM Pipeline Automation | Oracle One-Pass AutoML |
|---|---|---|
| Core Mechanism | Automated candidate pipeline generation and ranking using data allocation strategies based on seasonality and temporal dependence | Single-pass mechanism that measures temporal statistics of the series and asymmetrically allocates resources to the most promising algorithms |
| Search Strategy | Progressive testing of candidate pipelines; generates ranked lists via projected learning curves | Eliminates exhaustive search overhead through single-pass resource allocation to top candidates |
| Filing Jurisdictions | US (2024 pipeline generation) and WO (2022 pipeline ranking) | US (2026 one-pass approach) and US (2023 one-pass approach) |
| Filing Count in Dataset | 4 total filings (pipeline generation ×2, pipeline ranking, skills forecasting) | 3 total filings (one-pass AutoML ×2, synthetic sensor data ×1) |
| Application Target | General time series forecasting pipelines; also workforce/skills demand forecasting | Automated timeseries forecasting; synthetic high-fidelity sensor signal generation for ML development |
| Temporal Coverage in Dataset | 2022–2025 | 2019–2026 |
| Computational Approach | Data allocation strategy to progressively test candidates; projected learning curves for ranking | Asymmetric resource allocation based on measured temporal statistics — avoids full candidate evaluation |
Frequently Asked Questions: AutoML Industrial Time Series Forecasting
AutoML for industrial time series forecasting automates the end-to-end ML pipeline — from raw sensor data ingestion and feature engineering through model selection, hyperparameter tuning, and deployment — applied to temporally ordered data generated by industrial systems. It must address non-stationarity, irregular sampling, concept drift, multi-step prediction horizons, and real-time operational constraints.
In this dataset, IBM and ServiceNow each have 4 filings, Oracle, Microsoft, and Dell each have 3 filings, and Hewlett Packard Enterprise and Fujitsu each have 2 filings. IBM focuses on pipeline automation infrastructure; ServiceNow’s patents cover IT resource scheduling via time series ML.
The five core technical sub-domains identified in this dataset are: (1) pipeline automation and model selection, (2) feature engineering for temporal data, (3) ensemble and meta-learning methods, (4) edge and cloud-integrated deployment with drift detection, and (5) Transformer and deep learning architectures applied to multivariate sensor streams.
The US is dominant, accounting for approximately 30+ records. India is a notable secondary jurisdiction with at least 8 filings, including Mastercard’s AutoML system, Symbiosis International, and individual inventors. PCT (WO) filings appear across IBM, Oracle, GE, Microsoft, and Kinaxis. EP filings include Fujitsu, HPE, and UAB Rivile. DE and CN each have at least 1–2 filings.
The most recent filings reveal five vectors: edge-embedded AutoML with automated drift response (HPE, Fujitsu), Transformer architectures for multivariate industrial sensor streams (Symbiosis International), automated feature engineering as a standalone deployable module (GE Infrastructure Technology, Arti Analytics), federated learning combined with AutoML (Dodda, 2025, DE), and IIoT cold start forecasting using XGBoost for serverless edge computing (Kumar Tummala Suresh, 2026, IN).
According to literature within this dataset, two-step meta-learning using random forest regression on 390 time-series features outperformed Theta benchmarks on the M4 competition dataset. Ensemble voting regression combining Extra Trees, Random Forest, LightGBM, Gradient Boosting, and KNN outperformed individual models and ARIMA on energy forecasting tasks. Automotive OEM demand forecasting benchmarks found global ML models superior to local models across 21 algorithms.
Data and insights on this page are based on a limited patent and literature dataset and are for reference only. Figures may not represent the complete technology landscape.