---
# System prepended metadata

title: Timeseries Classification
tags: [HCMUT data_mining]

---

---
tags: HCMUT data_mining
---
Timeseries Classification
===

## Refs
http://www.timeseriesclassification.com/description.php?Dataset=Crop
https://towardsdatascience.com/time-series-classification-using-dynamic-time-warping-61dcd9e143f6
https://towardsdatascience.com/deep-learning-for-time-series-classification-inceptiontime-245703f422db
https://www.analyticsvidhya.com/blog/2019/01/introduction-time-series-classification/
https://towardsdatascience.com/a-brief-introduction-to-time-series-classification-algorithms-7b4284d31b97


## I. Timeseries data (0.5) (Khang + Khang)

## II. Algorithms (2) (Khanh + Khanh)
### Feature base (Quang Khanh)
Features can be extracted **globally** (over the entire time series) or **locally** (over regular intervals/bins, random intervals, sliding windows of intervals, and so on).
Series can be transformed into **primitive values** (e.g. mean, standard deviation, slope) or into other series (e.g. Fourier transform, series of fitted auto-regressive coefficients).

  - https://www.arxiv-vanity.com/papers/1709.08055/#:~:text=A%20feature%2Dbased%20approach%20to,of%20time%20series%20%5B35%5D%20.
  - https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-020-1661-4
    - Interval-based Classifiers
    - Dictionary-Based Classification
    - Bag of SFA Symbols (BOSS)
    - The BOSS Ensemble
    - Contractable BOSS (cBOSS)
    - Frequency-based
    - Shapelet-Based Classifiers
  
#### Distance base (Duy Khanh)
```
use distance metrics to determi ne class membership.
https://arxiv.org/abs/1806.04509

## DTW
- https://medium.com/walmartglobaltech/time-series-similarity-using-dynamic-time-warping-explained-9d09119e48ec
- https://rtavenar.github.io/blog/dtw.html
- https://staff.washington.edu/dbp/PDFFILES/FCS-overheads.pdf

## DWT
https://ataspinar.com/2018/12/21/a-guide-for-using-the-wavelet-transform-in-machine-learning/
https://towardsdatascience.com/the-wavelet-transform-e9cfa85d7b34
https://medium.com/financial-time-series-denoising-with-wavelet/introduction-to-wavelet-theory-ddafa4204707
https://en.wikipedia.org/wiki/Discrete_wavelet_transform
http://blogs.gm.fh-koeln.de/ciop/files/2019/01/thillwavelet.pdf
https://www.researchgate.net/figure/Discrete-wavelet-transform-applied-to-air-coupled-ultrasonic-probing-of-foams-From-top_fig4_245286628
```
![](https://i.imgur.com/zY1k9nF.png)

![](https://i.imgur.com/HfcN2g1.png)

  - dynamic time warping (DTW) metric: measures similarity between two sequences that may not align exactly in time, speed, or length.


### Ensemble Classifiers
  
### III. Applications (1.5) (Khang + Khang)
- List down application (0.5)
- Implement application (1)
