# Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT)

arXiv: https://arxiv.org/abs/1905.00125 · HTML: https://mlsanity.com/paper/1905.00125/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [SeqLink: A Robust Neural-ODE Architecture for Modelling Partially Observed Time Series](https://mlsanity.com/paper/2212.03560/) (2022)
- [PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series](https://mlsanity.com/paper/2110.00071/) (2021)
- [Imputing Missing Observations with Time Sliced Synthetic Minority Oversampling Technique](https://mlsanity.com/paper/2201.05634/) (2022)
- [Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks](https://mlsanity.com/paper/2302.04052/) (2023)
- [Recent Trends in Modelling the Continuous Time Series using Deep Learning: A Survey](https://mlsanity.com/paper/2409.09106/) (2024)
- [MuSiCNet: A Gradual Coarse-to-Fine Framework for Irregularly Sampled Multivariate Time Series Analysis](https://mlsanity.com/paper/2412.01063/) (2024)
- [AI-based detection of worsening heart failure from low-resolution telemonitoring data](https://mlsanity.com/paper/2609.29742/) (2026)

## Abstract (arXiv)

Missing values, irregularly collected samples, and multi-resolution signals commonly occur in multivariate time series data, making predictive tasks difficult. These challenges are especially prevalent in the healthcare domain, where patients' vital signs and electronic records are collected at different frequencies and have occasionally missing information due to the imperfections in equipment or patient circumstances. Researchers have handled each of these issues differently, often handling missing data through mean value imputation and then using sequence models over the multivariate signals while ignoring the different resolution of signals. We propose a unified model named Multi-resolution Flexible Irregular Time series Network (Multi-FIT). The building block for Multi-FIT is the FIT network. The FIT network creates an informative dense representation at each time step using signal information such as last observed value, time difference since the last observed time stamp and overall mean for the signal. Vertical FIT (FIT-V) is a variant of FIT which also models the relationship between different temporal signals while creating the informative dense representations for the signal. The multi-FIT model uses multiple FIT networks for sets of signals with different resolutions, further facilitating the construction of flexible representations. Our model has three main contributions: a.) it does not impute values but rather creates informative representations to provide flexibility to the model for creating task-specific representations b.) it models the relationship between different signals in the form of support signals c.) it models different resolutions in parallel before merging them for the final prediction task. The FIT, FIT-V and Multi-FIT networks improve upon the state-of-the-art models for three predictive tasks, including the forecasting of patient survival.