a collection of notebooks on time series classification projects as shown in YouTube channel @TimelyTimeSeries
(note: this repo is WIP because I'm thinking of moving all of my classification projects to this repo)
The models that I've tried so far:
- Random Convolutional Kernel for Time Series Classification (ROCKET). This model generates thousands of random kernels, and each of these captures a feature in the input data.
- MiniROCKET. This model is the extension of ROCKET, so its algorithm is really similar to ROCKET, except that they change some parameter values and use some tricks to allow a more efficient computation compared to ROCKET. The biggest advantage of this model is that it can be used for a varying length of time series.
- Multivariate LSTM - Fully Convolutional Network (MLSTMFCN). This model combines Long Short Term Memory (LSTM) network with a Fully Convolutional Network. The "M" in the beginning is for Multivariate; for univariate dataset, we can use the LSTMFCN model. I applied this model on a Human Activity Recognition Data (HAR) and a US Companies Bankruptcy dataset.