Flow Forecast: A deep learning for time series forecasting, classification, and anomaly detection framework built in PyTorch

2021-01-18 · Anonymous

Flow Forecast (FF) is a multi-purpose deep learning for time series forecasting, classification, and anomaly detection framework that contains state of the art time series models. Flow Forecast utilizes modular code design, unit/integration tests, model/prediction visualizations, and native cloud provider integration in order to allow researchers to rapidly experiment with new model architectures, benchmark their results on popular datasets and reproduce their results. Simultaneously it aids industry data scientists to deploy models to production, periodically retrain models, and explain model decisions to stakeholders.

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Tasks

Anomaly DetectionTime SeriesTime Series Analysis Time Series Forecasting

Addressing Distribution Shift in Time Series Forecasting with Instance Normalization Flows

2024-01-30 · Wei Fan, Shun Zheng, Pengyang Wang, Rui Xie 외

Due to non-stationarity of time series, the distribution shift problem largely hinders the performance of time series forecasting. Existing solutions either fail for the shifts beyond simple statistics or the limited com…

Time Series Time Series Forecasting

Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting

2026-02-02 · Mingyue Cheng, Xiaoyu Tao, Qi Liu, Ze Guo 외 arxiv

Time series forecasting has traditionally been formulated as a model-centric, static, and single-pass prediction problem that maps historical observations to future values. While this paradigm has driven substantial prog…

Time Series Forecasting Reinforcement Learning

FlowScope: Enhancing Decision Making by Time Series Forecasting based on Prediction Optimization using HybridFlow Forecast Framework

2024-11-16 · Nitin Sagar Boyeena, Begari Susheel Kumar

Time series forecasting is crucial in several sectors, such as meteorology, retail, healthcare, and finance. Accurately forecasting future trends and patterns is crucial for strategic planning and making well-informed de…

Decision Making State Space Models Time Series Time Series Forecasting

Deep Learning with Kernel Flow Regularization for Time Series Forecasting

2021-09-23 · Mahdy Shirdel, Reza Asadi, Duc Do, Micheal Hintlian

Long Short-Term Memory (LSTM) neural networks have been widely used for time series forecasting problems. However, LSTMs are prone to overfitting and performance reduction during test phases. Several different regulariza…

Deep Learning Time Series Time Series Analysis Time Series Forecasting

DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Series

2025-11-04 · Dongze Wu, Feng Qiu, Yao Xie arxiv

Time-series forecasting increasingly demands not only accurate observational predictions but also causal forecasting under interventional and counterfactual queries in multivariate systems. We present DoFlow, a flow-base…

Anomaly Detection