Time Series Classification
Time Series Classification is a general task that can be useful across many subject-matter domains and applications. The overall goal is to identify a time series as coming from one of possibly many sources or predefined groups, using labeled training data. That is, in this setting we conduct supervised learning, where the different time series sources are considered known.
Source: Nonlinear Time Series Classification Using Bispectrum-based Deep Convolutional Neural Networks
Papers
Showing 1–10 of 697 papers
All datasetsPhysioNet Challenge 2012pendigitsArabicDigitsJapaneseVowelsLibrasUWaveWaferAUSLANCharacterTrajectoriesCMUsubject16DigitShapesECG
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | GP-Sig-GRU | Accuracy | 1 | — | Unverified |
| 2 | GP-Sig-LSTM | Accuracy | 1 | — | Unverified |
| 3 | MALSTM-FCN | Accuracy | 1 | — | Unverified |
| 4 | SNLST | Accuracy | 1 | — | Unverified |
| 5 | FCN-SNLST | Accuracy | 1 | — | Unverified |
| 6 | GP-GRU | Accuracy | 0.99 | — | Unverified |
| 7 | GP-Sig | Accuracy | 0.98 | — | Unverified |
| 8 | GP-LSTM | Accuracy | 0.92 | — | Unverified |
| 9 | GP-KConv1D | Accuracy | 0.9 | — | Unverified |