FITS: Modeling Time Series with $10k$ Parameters

In this paper, we introduce FITS, a lightweight yet powerful model for time series analysis. Unlike existing models that directly process raw time-domain data, FITS operates on the principle that time series can be manipulated through interpolation in the complex frequency domain, achieving performance comparable to state-ofthe-art models for time series forecasting and anomaly detection tasks. Notably, FITS accomplishes this with a svelte profile of just about 10k parameters, making it ideally suited for edge devices and paving the way for a wide range of applications. The code is available: https://github.com/VEWOXIC/FITS. Time series analysis plays a pivotal role in a myriad of sectors, from healthcare appliances to smart factories. Within these domains, the reliance is often on edge devices like smart sensors, driven by MCUs with limited computational and memory resources. Time series data, marked by its inherent complexity and dynamism, typically presents information that is both sparse and scattered within the time domain. To effectively harness this data, recent research has given rise to sophisticated models and methodologies (Zhou et al., 2021; Liu et al., 2022a; Zeng et al., 2023; Nie et al., 2023; Zhang et al., 2022). Yet, the computational and memory costs of these models makes them unsuitable for resource-constrained edge devices. On the other hand, the frequency domain representation of time series data promises a more compact and efficient portrayal of inherent patterns. While existing research has indeed tapped into the frequency domain for time series analysis -- FEDformer (Zhou et al., 2022a) enriches its features using spectral data, and TimesNet (Wu et al., 2023) harnesses high-amplitude frequencies for feature extraction via CNNs -- a comprehensive utilization of the frequency domain's compactness remains largely unexplored. Specifically, the ability of the frequency domain to employ complex numbers in capturing both amplitude and phase information is not utilized, resulting in the continued reliance on compute-intensive models for temporal feature extraction. In this study, we reinterpret time series analysis tasks, such as forecasting and reconstruction, as interpolation exercises within the complex frequency domain.