How are time series features encoded in data?
As Data Scientists, we must do our best to extract time patterns and make our data speak themself. During preprocessing, common procedures are for example standardization (stationarity check, autocorrelation removal…), creating and encoding categorical time features (day, week, month, season…), manual feature engineering (Fourier transformations…).
How to encode univariate time series as images?
As univariate time series are in 1D and the dot product fails to distinguish the valuable informations from Gaussian noise, there is no other way to take advantage of “angular” relations than changing the space. We must therefore encode the time serie into a space of at least 2 dimensions, prior to using Gram Matrix like constructs.
How to make predictions for time series forecasting with?
This is how well we expect the model to perform on average when making forecasts on new data. Finally, a graph is created showing the actual observations in the test dataset (blue) compared to the predictions (red). This may not be the very best possible model we could develop on this problem, but it is reasonable and skillful.
How to use classical machine learning for time series prediction?
I want to use classical machine learning models such XGBoost for my time series prediction. Since the input data for XGBoost/sklearn based models is 2d i.e. (n_samples, n_features), I want to encode Feature selections on replicates?
Which is better T2V or LSTM for time series encoding?
The best and optimized T2V + LSTM achieves around 1.67 MAE on the test, while the simple and optimized LSTM achieves around 2.02. Both the networks seem to be able to identify the pattern in the data really well.
Do you need data in time series format?
To give evidence and a concrete utility of the whole solution, we need an adequate dataset. In my case, I need data in time series format without any redundant information in form of extra features.
Is there a new model for time series analysis?
The authors don’t want to propose a new model for time series analysis, but instead, their goal is to provide a representation of time, in form of vector embedding, in order to automatize the feature engineering process and model time in a better way. To give evidence and a concrete utility of the whole solution, we need an adequate dataset.