Time Series Forecasting

gistfile1.txt

Time Series Forecasting Techniques
Smoothing Based Techniques:
Simple Moving Average
Simple Exponential Smoothing
Holt’s Linear Trend
Holt Winter’s Exponential Smoothing

ARIMA Based Techniques:
AR
MA
ARMA
ARIMA
SARIMA
ARIMAX
SARIMAX

Additional Case Specific Techniques
Random Walk
Unobserved Component Model
Vector Autoregressive Model


p: AutoRegressive (AR) term
   - Represents the number of past values (lags) used to predict future values.
   - Example: If p=2, the model uses the last 2 values to predict the next one.

d: Differencing (I) term
   - Represents the number of times the data needs to be differenced to become stationary.
   - Example: If d=1, we use the first difference (current value - previous value).

q: Moving Average (MA) term
   - Represents the number of past error terms included in the model.
   - Example: If q=2, the model uses the last 2 forecast errors to adjust predictions.





prophet - Meta model for forecasting
Many statistical and machine learning models, such as ARIMA, assume that the time series is stationary.