Pattern recognition algorithms used in data science
- K-Nearest Neighbors (KNN)
- Linear Discriminant Analysis (LDA)
- Quadratic Discriminant Analysis (QDA)
- Decision Trees
- Random Forest
- Naive Bayes
- Support Vector Machines (SVMs)
- Neural Networks (including Deep Learning)
- k-means
- Hierarchical clustering
- DBSCAN
- Principal Component Analysis (PCA)
- Independent Component Analysis (ICA)
- Non-Negative Matrix Factorization (NMF)
- Singular Value Decomposition (SVD)
With time series data, some common pattern recognition algorithms include
- Time Series Decomposition: decomposing a time series into its components such as trend, seasonality and noise
- Exponential smoothing: used for forecasting and estimating the trend in time series data
- ARIMA: a class of statistical models for analyzing and forecasting time series data
- Seasonal decomposition of time series by Loess (STL): decompose time series into seasonal, trend, and residual components
- Dynamic Time Warping (DTW): a technique for measuring similarity between two temporal sequences, often used in time series classification
- Hidden Markov Models (HMM): used for modeling sequential data, such as stock prices or speech signals.
- Additionally, Recurrent Neural Networks (RNN) and its variants such as LSTM and GRU are also very effective in time series analysis and prediction tasks.
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