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BUILDER'S SANDBOX

Core Pattern

AI-generated implementation pattern based on this paper's core methodology.

Implementation pattern included in full analysis above.

MVP Investment

$9K - $12K
6-10 weeks
Engineering
$8,000
Cloud Hosting
$240
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

2-4x

3yr ROI

10-20x

Lightweight AI tools can reach profitability quickly. At $500/mo average contract, 20 customers = $10K MRR by 6mo, 200+ by 3yr.

Talent Scout

X

Xihao Piao

Osaka University

Z

Zheng Chen

Osaka University

L

Lingwei Zhu

Great Bay University

Y

Yushun Dong

Florida State University

Find Similar Experts

Time experts on LinkedIn & GitHub

Founder's Pitch

"Develop a plug-and-play forecasting enhancer that improves nonstationary time series predictions by leveraging frequency domain insights."

Time Series AnalysisScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

4/4 signals

10

Series A Potential

2/4 signals

5

🔭 Research Neighborhood

Generating constellation...

~3-8 seconds

Why It Matters

Time series forecasting is critical in industries like finance and manufacturing, where predictive accuracy impacts decision-making and operational efficiency. However, non-stationarities often degrade model performance, necessitating advanced methods like TIFO to handle these variances robustly.

Product Angle

Productize TIFO as a software library or API that can be incorporated into existing time series models, enabling businesses to mitigate distributional shifts and improve forecasting accuracy without needing extensive retraining of models.

Disruption

This approach could replace or enhance traditional normalization methods and time domain models that currently handle non-stationarity, offering a more effective alternative that integrates seamlessly with existing systems.

Product Opportunity

The time series analysis market is vast, with applications in finance, supply chain management, and energy. Customers are companies leveraging predictive analytics that are facing challenges with non-stationary data, who would pay for improved forecasting accuracy.

Use Case Idea

Integrate TIFO as an enhancement layer in existing demand forecasting software to improve the accuracy of predicting stock levels in rapidly changing market conditions.

Science

TIFO introduces a frequency-based learning model for time series, which applies a time-invariant frequency operator to emphasize stable frequency components and suppress non-stable ones. This is achieved through a two-step process using Fourier transforms to adapt frequency weights based on stationarity, reducing distribution shifts.

Method & Eval

TIFO is evaluated on datasets like ETTm2, demonstrating significant accuracy improvements (33.3% and 55.3% in MSE) and reducing computational costs by up to 70%, showing strong fractional gains over baseline methods.

Caveats

Limitations could include overemphasis on frequency characteristics, potential overspecialization for specific datasets, or challenges scaling for extremely high-dimensional data.

Author Intelligence

Xihao Piao

Osaka University
park88@sanken.osaka-u.ac.jp

Zheng Chen

Osaka University
chenz@sanken.osaka-u.ac.jp

Lingwei Zhu

Great Bay University
zhulingwei@gbu.edu.cn

Yushun Dong

Florida State University
yd24f@fsu.edu

Yasuko Matsubara

Osaka University
yasuko@sanken.osaka-u.ac.jp

Yasushi Sakurai

Osaka University
yasushi@sanken.osaka-u.ac.jp

References (40)

[1]
Frequency Adaptive Normalization For Non-stationary Time Series Forecasting
2024Weiwei Ye, Songgaojun Deng et al.
[2]
FRNet: Frequency-based Rotation Network for Long-term Time Series Forecasting
2024Xinyu Zhang, Shanshan Feng et al.
[3]
Deep Time Series Models: A Comprehensive Survey and Benchmark
2024Yuxuan Wang, Haixu Wu et al.
[4]
Not All Frequencies Are Created Equal: Towards a Dynamic Fusion of Frequencies in Time-Series Forecasting
2024Xingyu Zhang, Siyu Zhao et al.
[5]
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
2024Haoxin Liu, Harshavardhan Kamarthi et al.
[6]
Fredformer: Frequency Debiased Transformer for Time Series Forecasting
2024Xihao Piao, Zheng Chen et al.
[7]
FreDF: Learning to Forecast in the Frequency Domain
2024Hao Wang, Licheng Pan et al.
[8]
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
2023Kun Yi, Qi Zhang et al.
[9]
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
2023Yong Liu, Tengge Hu et al.
[10]
FITS: Modeling Time Series with 10k Parameters
2023Zhijian Xu, Ailing Zeng et al.
[11]
TSMixer: An all-MLP Architecture for Time Series Forecasting
2023Si Chen, Chun-Liang Li et al.
[12]
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting
2023Wei Fan, Pengyang Wang et al.
[13]
PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction
2023Jiawei Jiang, Chengkai Han et al.
[14]
Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective
2023Zhiding Liu, Mingyue Cheng et al.
[15]
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting
2023Yunhao Zhang, Junchi Yan
[16]
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
2022Yuqi Nie, Nam H. Nguyen et al.
[17]
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
2022Haixu Wu, Teng Hu et al.
[18]
Are Transformers Effective for Time Series Forecasting?
2022Ailing Zeng, Mu-Hwa Chen et al.
[19]
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
2022Tian Zhou, Ziqing Ma et al.
[20]
ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
2022Gerald Woo, Chenghao Liu et al.

Showing 20 of 40 references