Time series analysis for blayer pharm

Document Type:Essay

Subject Area:Health Care

Document 1

Apart from the methods above the winter’s method can be used however data required to use this method is not provided in the case study. Method 1: Moving Average Using the moving average method to calculate the average centered moving average for the dataset using the week data provided. Firsts we define the data to get the periodicity of the data where 1 week is defined as 7 days. Then create a time series moving average for the first product BP Cuff Type 1 the results are shown below in the screenshot of the SPSS table. From the moving average we can calculate the forecast for the Results for type 1 cuff: BP Model Description Model Name MOD_1 Series or Sequence 1 BP Cuff Type 1 2 MA(BPCuffType1,3,3) Transformation None Non-Seasonal Differencing 0 Seasonal Differencing 0 Length of Seasonal Period 7 Horizontal Axis Labels Week Intervention Onsets None For Each Observation Values joined Applying the model specifications from MOD_1 Case Processing Summary BP Cuff Type 1 MA(BPCuffType1,3,3) Series or Sequence Length 70 70 Number of Missing Values in the Plot User-Missing 0 0 System-Missing 0 2 Forecast Model BP Cuff Type 1-Model_1 BP Cuff Type 2-Model_1 Forecast.

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70% for cuff type 2. Method 2: Single exponential smoothing forecast method. For sales of type 1 cuff for the next 30 days: Model Description Model Type Model ID BP Cuff Type 1 Model_1 Simple Seasonal Model Summary Model Statistics Model Number of Predictors Model Fit statistics Ljung-Box Q(18) Number of Outliers MAPE MAE MaxAPE Statistics DF Sig. BP Cuff Type 1-Model_1 0 4. 011 0 Exponential Smoothing Model Parameters Model Estimate SE t Sig. 33 MAPE type 2 2. 83 Using the single exponential smoothing method the forecast sales of type 1 cuff for next 30 days is 499. 9 and the mean absolute percentage error is 4. 33% for cuff 1 and 830. 9 and mean absolute percentage error of 2. 487 N 70 70 BP Cuff Type 2 Pearson Correlation -. 070 1 Sig. (2-tailed). 563 Sum of Squares and Cross-products -7899. 071 Covariance -114. 000 Sig. (2-tailed). N 70 70 Reference Albright, S.

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