Time Series Components and Stationarity
Trend, seasonality, noise, ADF test for stationarity, differencing to achieve stationarity.
What Is a Time Series?
A time series is data indexed in time order, such as daily sales or hourly temperature. Because observations are ordered and often correlated, special techniques are needed; you cannot just shuffle the rows like ordinary tabular data.
The Three Components
A classic view decomposes a series into:
- Trend: long-term upward or downward movement.
- Seasonality: repeating patterns at fixed periods (weekly, yearly).
- Residual: the leftover random noise after removing trend and seasonality.
All lessons in this course
- Time Series Components and Stationarity
- ARIMA and SARIMA Models
- Prophet for Automated Forecasting
- LSTM for Time Series Forecasting