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ARIMA is the most popular model used for time series analysis and forecasting. Despite being so popular among the community, it has certain limitations as well.
Chi-Square test is used to know dependency and to examine fitness of categorical variables.
The exponential smoothing and moving average are the two basic and important techniques used for time series forecasting.
ICA finds independent components rather than uncorrelated component.
GIT is pre-trained using the BERT encoder and KERMIT objective on an unsupervised LM task.
Curriculum learning is also a type of machine learning that trains the model in such a way that humans get trained using their education system
The statistical features of a time series could be made stationary by differencing method.
OPTICS is a density-based clustering algorithm offered by Pyclustering.
There can be various reason behind a neural network fails to converge. failure in convergence can make us confuse about the model results.
The sklearn package provides a mechanism to standardize data transformations.
t-SNE is a nonlinear dimensionality technique that can be utilized in a scenario where the data is very high dimensional.