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Time Series - Coggle Diagram

- - - - TS can be tested for stationarity!
        rolling statistic, Augmented Dickey Fuller tests, etc
      - Technics to convert to S
        
        Differencing
        
        Several orders of the basic:
        difference = previous observation - current observation
        subtraction prev from current obs
        
        Transformation
        
        Taking log, roots, etc from obs, depending on the
        present trend
        
        Moving Average
        
        Several (= window width) data pts generate one value
        
        Weighted moving avg
        
        Distant points values less then more recent
        
        Centered Moving Average
        
        Trailing Moving Average
        
        t-2, t-1, t
    - - When statistical properties (mean, std) change over time
        and there is no trend or seasonality
        
        Affects training methodology!!
  - - - single RV
    - - multiple random variables
- - - - Fixed partitioning
        
        Data = fixed chunks of T/V/T
        80 train + 10 val + 10 test
      - Roll-forward partitioning
        
        Add more data to training gradually
        week-by-week for e.g.
  - - - vt = v(t-1)
    - - vt = avg(v(t-X)..v(t-1))
- - - - Step 7: At the end we can do the future forecasting and get the future forecasted values in original scale.
      - Step 6: Now we will have an array of predictions which are in transformed scale. We just need to apply the reverse transformation to get the prediction values in original scale.
      - Step 5: We can assess the performance of a model by applying simple metrics such as residual sum of squares(RSS). Make sure to use whole data for prediction.
      - Step 4: Based on data analysis choose the appropriate model for time series forecasting
      - Step 3: Note down the transformation steps performed to make the time series stationary and make sure that the reverse transformation of data is possible to get the original scale back
      - Step 2: Do the analysis and identify the best method to make the time series stationary
      - Step 1: Understand the time series characteristics like trend, seasonality etc
  - - - Autoregression (AR)
      - Moving Average (MA)
        
        Rather than using past values of the forecast variable in a regression, a moving average model uses linear combination of past forecast errors
        
        To figure out the order of MA model we will use ACF function
      - Autoregressive Moving Average (ARMA)
      - Autoregressive Integrated Moving Average (ARIMA)
        
        ARIMA stands for Auto-Regressive Integrated Moving Averages. It is actually a combination of AR and MA model.
        
        ARIMA has three parameters 'p' for the order of Auto-Regressive (AR) part, 'q' for the order of Moving Average (MA) part and 'd' for the order of integrated part.
      - Seasonal Autoregressive Integrated Moving-Average (SARIMA)
      - Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors (SARIMAX)
      - Vector Autoregression (VAR)
      - Vector Autoregression Moving-Average (VARMA)
      - Vector Autoregression Moving-Average with Exogenous Regressors (VARMAX)
      - Simple Exponential Smoothing (SES)
      - Holt Winter’s Exponential Smoothing (HWES)