23 Time series

Time series is a sequence taken at successive equally spaced points in time (from Wikipedia).

Machine Learning modals in Time Series:
1) Autoregression (AR)
2) Moving Average (MA)
3) Autoregressive Moving Average (ARMA)
4) Autoregressive Integrated Moving Average (ARIMA)
5) Seasonal Autoregressive Integrated Moving-Average (SARIMA)
6) Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors (SARIMAX)
7) Vector Autoregression (VAR)
8) Vector Autoregression Moving-Average (VARMA)
9) Vector Autoregression Moving-Average with Exogenous Regressors (VARMAX)
10) Simple Exponential Smoothing (SES)
11) Holt Winter’s Exponential Smoothing (HWES)

Basic intuition is given in the below web.
https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/


https://towardsdatascience.com/an-overview-of-time-series-forecasting-models-a2fa7a358fcb
Naïve, SNaïve
Seasonal decomposition (+ any model)
Exponential smoothing

GARCH
https://en.wikipedia.org/wiki/Autoregressive_conditional_heteroskedasticity
https://cran.r-project.org/web/packages/rugarch/vignettes/Introduction_to_the_rugarch_package.pdf

Dynamic linear models
TBATS
Prophet

NNETAR(Neural NETwork AutoRegression.)
https://otexts.com/fpp2/nnetar.html


LSTM