====== 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.