**Time Series Basics**

**Time series can have three components**

- Trend
- Seasonality
- Random fluctuations (noise)

**Stationary Time series**

- Mean, Variance, Covariance remains constant over time (needs to remove trend, seasonal components) from time series as certain model works on Stationary time series
- To make series stationary , take log, differencing (or both), square root, log transformation

**Model that requires Time series has to be stationary**

- AR(p) — p is number of last lag’s data value taken in account for predicting
- MA(q) — q is number last lag’s error taken into account predicting
- ARIMA (p,d,q) — d for differencing to remove trend and seasonal effect
- SARIMA (p,d,q)(P,D,Q)s — s is frequency for seasonality

**Model requires to know value of p and q that is determined by plotting**

- ACF — Auto Correlation Plot (used for MA model to decide for q value, the number from which correlation cuts-off to very insignificant value)
- PCF — Partial Correlation Plot (used for AR model to decide for p value, the number from which correlation cuts-off to very insignificant value)

**Models that Doesn’t assume that time series stationary**

- Holt models (models around level (mean) alpha, trend (beta)) — when time series has no seasonal component
- Holt Winters model (same as above + Seasonal component (gamma)
- ARCH model (based on Variance)