Activity 2: Forecast Technique AnalysisForecasting technique that best fits the data:Copyrate 02025BeromidactionI bracybolor Select3-Month Moving ForecastExponential Smoothing Forecast for 2Exponential Smoothing Forecast for .5Nailve ForecastSeasonal ForecastStepsRead scenario and approach.In Activity 1 you will need to forecast for each month of Year 2, beginning in January.The Year 1 Table provides Actual Demand as well as the demand that forecasted using 5 different forecasting techniques: Naive, 3-Month Moving Average, Exponential Smoothing for .2, Exponential Smooth for .5, and Seasonal. From month to month, different techniques may be more effective, which you can tell from the Forecast Error. The most effective technique is the one with a Forecast Error closest to 0.Note that the Year 2 table will fill out automatically as you input your forecasts in the Activity section.To get started with your own forecast:a. Review the Year 1 data. Look especially at the latest month of data to identify which forecasting strategy was most effective for that month.b. Use the JAN Year 2 input box in the Activity section to input your forecast. Inpus the forecast from the strategy that performed best in the previous month. That month Actual Demand will automatically be filled out for you.c. In the Year 2 table you can see how the different forecasting strategies performed. Remember you want to use the strategy that was most effective for the latest month. Repeat the steps each month to complete the Activity inputs.For Activity 2, review your forecasts and identify which forecasting technique best fit the data based on overall performance. (Typically the technique you used most frequently).Click the sum button to keep your progress. Once you've finished the assignmenc, click the button to receive your grade and feedback.ScenarioYou have been given a task to create a demand forecast for the second year of sales of a premium outdoor gill. Accurate forecasts are important for many reasons, including for the company to ensure they have the materials they need to create the products required in a certain period of time. Your objective is to minimise the forecast error, which wit be measured using the Mean Absolute Percentage Error (MAPE) with a goal of being below 25%.You have hatorical monthly sales data for the past year and access to software that provides forecasts based on five different forecasting techniques (Naive,3-Month Moving Average, Exponential Smoothing for 2, Exponential Smooth for 5, and Seasonal) to help determine the best forecast for that particular month. Based on the given data, you will identily trends and patterns to create a more accurate forecast.ApproachConsider the previous monith's forecast to identily which technique is most effective. Use that to forecast the next month.Remember to select the forecasting technique that produces the forecast error nearest to zero. For example:a. Nailve Forecast is 230 and the Forecast Error is -15.b.3-Month Moving Forecast is 290 and the Forecast Error is -75.c. Exponential Smoothing Forecast for 2 is 308 and the Forecast Error is -93.d. Exponential Smoothing Forecast for 5 is 279 and the Forecast Error is -4.e. Seasonal Forecast is 297 and the Forecast Error is -82.The forecast for the next month would be 230 as the Naive forecast had the Forecast Error closest to zero with a -15. This forecasting technique was the best performing technique for that month. You do not need to do any enternal analysis-the forecast error for each strategy is already calculated for you in the tablis below.Activity 1: Year 2 Forecast\table[[,Year 2,MAPLW,,Actual Demand,],[Forecast meat period,,,Average MAPE,Monch,Year 1,Vear 2,Statsonal Index],[JAN,,,,JAN,78,,0.954],[FES,,,,48,83,,0,848],[MAR,,,,MAR,78,,0.901],[APR,,,,APR,158,,1,033],[MAY,,,,MAY,113,,1.298],[JUN,,,,JUN,117,,1.219],[M,,,,,,97,,1.139],[me,,,,AUS,117,,1.153],[STPT,,,,SEPT,83,,1.033],[oct,,,,OCT,74,,0.848],[NOV,,,,NOV,83,,0.869],[DEC,,,,Dec,24,,0.758]]