ExcelR Data Science Assignment No 18

• Forecasting is a data science task that is critical to a variety of activities within any business organisation. Forecasting is a useful tool that can help to understand how historical data influences the future. This is done by looking at past data, defining the patterns, and producing short or long-term predictions.

1. Trend : Increase or decrease in the series of data over longer a period.

2. Seasonality : Fluctuations in the pattern due to seasonal determinants over a period such as a day, week, month, season.

3. Cyclical variations : Occurs when data exhibit rises and falls at irregular intervals.

4. Random or irregular variations : Instability due to random factors that do not repeat in the pattern.

1. Autoregressive (AR)

2. Moving Average (MA)

3. Autoregressive Integrated Moving Average (ARIMA)

4. Seasonal Autoregressive Integrated Moving Average (SARIMA)

5. Exponential Smoothing (ES)

Forecast the Airlines Passengers data set. Prepare a document for model explaining. How many dummy variables you have created and RMSE value for model. Finally which model you will use for Forecasting.

Forecast the CocaCola prices data set. Prepare a document for model explaining. How many dummy variables you have created and RMSE value for model. Finally which model you will use for Forecasting.