# Feature-based time-series analysis

arXiv: https://arxiv.org/abs/1709.08055 · HTML: https://mlsanity.com/paper/1709.08055/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2017.

## Built on by (most cited first, 28 of 28)

- [It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks](https://mlsanity.com/paper/2602.12147/) (2026)
- [A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting](https://mlsanity.com/paper/2206.08998/) (2022)
- [TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning](https://mlsanity.com/paper/2405.03140/) (2024)
- [Unifying Pairwise Interactions in Complex Dynamics](https://mlsanity.com/paper/2201.11941/) (2022)
- [Novel Features for Time Series Analysis: A Complex Networks Approach](https://mlsanity.com/paper/2110.09888/) (2021)
- [An Empirical Evaluation of Time-Series Feature Sets](https://mlsanity.com/paper/2110.10914/) (2021)
- [Feature-Based Time-Series Analysis in R using the theft Package](https://mlsanity.com/paper/2208.06146/) (2022)
- [Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification](https://mlsanity.com/paper/2303.17809/) (2023)
- [Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets](https://mlsanity.com/paper/2204.06540/) (2022)
- [Classification of Stochastic Processes with Topological Data Analysis](https://mlsanity.com/paper/2206.03973/) (2022)
- [An ensemble of convolution-based methods for fault detection using vibration signals](https://mlsanity.com/paper/2305.05532/) (2023)
- [Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting](https://mlsanity.com/paper/2407.19784/) (2024)
- [ELATE: Evolutionary Language model for Automated Time-series Engineering](https://mlsanity.com/paper/2508.14667/) (2025)
- [Estimating Time Series Foundation Model Transferability via In-Context Learning](https://mlsanity.com/paper/2509.23695/) (2025)
- [Analyzing categorical time series with the R package ctsfeatures](https://mlsanity.com/paper/2304.12332/) (2023)
- [Parameter inference from a non-stationary unknown process](https://mlsanity.com/paper/2407.08987/) (2024)
- [Learning Global and Local Features of Power Load Series Through Transformer and 2D-CNN: An Image-based Multi-step Forecasting Approach Incorporating Phase Space Reconstruction](https://mlsanity.com/paper/2407.11553/) (2024)
- [Interpretable Transformation and Analysis of Timelines through Learning via Surprisability](https://mlsanity.com/paper/2503.04502/) (2025)
- [FEG-Pro: Forecast-Error Growth Profiling for Finite-Horizon Instability Analysis of Nonlinear Time Series](https://mlsanity.com/paper/2605.17282/) (2026)
- [Hydroclimatic time series features at multiple time scales](https://mlsanity.com/paper/2112.01447/) (2021)
- [FRANS: Automatic Feature Extraction for Time Series Forecasting](https://mlsanity.com/paper/2209.07018/) (2022)
- [A Dynamic Feedforward Control Strategy for Energy-efficient Building System Operation](https://mlsanity.com/paper/2302.10179/) (2023)
- [Path Signatures and Graph Neural Networks for Slow Earthquake Analysis: Better Together?](https://mlsanity.com/paper/2402.03558/) (2024)
- [Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data](https://mlsanity.com/paper/2407.08003/) (2024)
- [SPINEX-TimeSeries: Similarity-based Predictions with Explainable Neighbors Exploration for Time Series and Forecasting Problems](https://mlsanity.com/paper/2408.02159/) (2024)
- [An Explainable Neural Radiomic Sequence Model with Spatiotemporal Continuity for Quantifying 4DCT-based Pulmonary Ventilation](https://mlsanity.com/paper/2503.23898/) (2025)
- [LLM-Guided ODE Discovery and Parameter Inference from Small-Cohort Aggregate Data](https://mlsanity.com/paper/2607.00733/) (2026)
- [Statistical comparisons of time-series feature sets on classification tasks](https://mlsanity.com/paper/2608.01586/) (2026)

## Abstract (arXiv)

This work presents an introduction to feature-based time-series analysis. The time series as a data type is first described, along with an overview of the interdisciplinary time-series analysis literature. I then summarize the range of feature-based representations for time series that have been developed to aid interpretable insights into time-series structure. Particular emphasis is given to emerging research that facilitates wide comparison of feature-based representations that allow us to understand the properties of a time-series dataset that make it suited to a particular feature-based representation or analysis algorithm. The future of time-series analysis is likely to embrace approaches that exploit machine learning methods to partially automate human learning to aid understanding of the complex dynamical patterns in the time series we measure from the world.