GitHub

An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection

TSB-UAD is a new open, end-to-end benchmark suite to ease the evaluation of univariate time-series anomaly detection methods. Overall, TSB-UAD contains 12686 time series with labeled anomalies ...
IEEE

Machine Learning for Univariate Time Series Imputation

Abstract: Missing data occur in almost real time series applications. Using incomplete data or ignoring missing values can cause inaccurate results and reduce system efficiency. Recovering missing ...
GitHub

Prometheus exporter that mines /proc to report on selected processes.

Some apps are impractical to instrument directly, either because you don't control the code or they're written in a language that isn't easy to instrument with Prometheus. We must instead resort to ...
IEEE

LSTM Deep Learning vs ARIMA Algorithms for Univariate Time Series Forecasting: A case study

Abstract: This manuscript aims to study and compare the Long Short-Term Memory (LSTM) Deep learning to Auto regressive Integrated Moving Average (ARIMA) algorithms for a univariate time series, ...