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.