Summary of Variational Mode Decomposition and Linear Embeddings Are What You Need For Time-series Forecasting, by Hafizh Raihan Kurnia Putra et al.
Variational Mode Decomposition and Linear Embeddings are What You Need For Time-Series Forecasting
by Hafizh Raihan Kurnia Putra, Novanto Yudistira, Tirana Noor Fatyanosa
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a robust time-series forecasting framework that integrates Variational Mode Decomposition (VMD) with linear models to mitigate data volatility and enhance forecast accuracy. The proposed approach is evaluated on 13 diverse datasets, including ETTm2, WindTurbine, M4, and air quality datasets from Southeast Asian cities. The study compares Root Mean Squared Error (RMSE) values between VMD-integrated models and those without it, demonstrating a significant reduction in RMSE across nearly all models. Furthermore, the paper benchmarks linear-based models against neural network architectures like LSTM, Bidirectional LSTM, and RNN, showing that the Linear + VMD model achieved the lowest average RMSE in univariate forecasting (0.619) and the DLinear + VMD model outperformed others in multivariate forecasting with an average RMSE of 0.019. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions about things that happen over time, like weather or air quality. The problem is that data can be unstable and this makes it hard to predict what will happen next. To fix this, the authors use something called Variational Mode Decomposition (VMD) to break down the data into different parts. They then combine this with simple math models to make more accurate predictions. They tested their approach on lots of different datasets and found that it worked really well. In fact, they were able to predict things more accurately than some other popular methods. |
Keywords
» Artificial intelligence » Lstm » Neural network » Rnn » Time series
