ExcelR_Assignment---Forecasting---Assignment---18

Forecasting :

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.

There are four general components that a time series forecasting model is comprised of :

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.

Popular Algorithms :

1. Autoregressive (AR)

2. Moving Average (MA)

3. Autoregressive Integrated Moving Average (ARIMA)

4. Seasonal Autoregressive Integrated Moving Average (SARIMA)

5. Exponential Smoothing (ES)

This assignment will study following Questions :

Problem Statement No 1 :

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.

Problem Statement No 2 :

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.