Data Science - Forecasting
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 :
Trend : Increase or decrease in the series of data over longer a period.
Seasonality : Fluctuations in the pattern due to seasonal determinants over a period such as a day, week, month, season.
Cyclical variations : Occurs when data exhibit rises and falls at irregular intervals.
Random or irregular variations : Instability due to random factors that do not repeat in the pattern.
Popular Algorithms :
Autoregressive (AR)
Moving Average (MA)
Autoregressive Integrated Moving Average (ARIMA)
Seasonal Autoregressive Integrated Moving Average (SARIMA)
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.