Time Series Analysis
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A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
Automatically extracts and selects relevant features from time series data for machine learning tasks.
An R package for robust anomaly detection in time series and vectors, handling seasonality and trend.
A Python machine learning toolkit for time series analysis with scikit-learn compatible API.
A collection of Python notebooks and tools for quantitative finance research, including backtesting, machine learning, and portfolio optimization.
A curated list of research papers, datasets, and resources for anomaly detection in time-series, video, and image data.
A Python library for outlier, adversarial, and drift detection in machine learning models, supporting tabular, text, image, and time series data.
An R package for estimating causal effects in time series using Bayesian structural time-series models.
A lightweight Python library for anomaly detection and correlation in time series data, enabling root cause analysis.
An open-source ML-powered analytics engine for automated outlier detection and root cause analysis on high-dimensional metrics.
An R package for detecting statistically significant breakpoints in time series using robust energy statistics.
Experimental implementations of financial machine learning techniques from 'Advances in Financial Machine Learning' for stochastic time series data.
Code repository for the second edition of Mastering Python for Finance, implementing advanced financial statistical applications using Python.
A lightweight Julia toolkit for working with time series data, providing efficient data structures and operations.
An end-to-end Python outlier detection system with database support, automated machine learning, and unified APIs for statistical, ML, and deep learning models.
Deep reinforcement learning framework for financial trading using price trailing, implemented with Keras-RL for Forex markets.
A Python probabilistic programming framework for objective model selection in time-varying parameter time series models.
An open-source framework for developing large-scale anomaly detection models using Apache Spark.
A Python library for unsupervised learning of hidden semi-Markov models with explicit durations.
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