Forecasting with Indices Summer
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- Date Submitted: 03/22/2011 03:38 PM
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Forecasting with Indices-Summer Historical Inventory Data
Introduction
Using the summer historical inventory data, I have used the seasonal variation which refers to periodic increases or decreases that occur within a calendar year in a time series. For the most part they are very predictable because they occur every year. This analysis will include converting data into an index and using the time series data from the index to forecast inventory for the fifth year.
Seasonal Variation
Seasonal variation is very predictable because they occur every year. When a time series consists of annual data, you cannot see what is going on within each year. Data reported in annual increments therefore cannot be used to examine seasonality. When time series data are monthly as it is in this case, seasonal variation may be evident. For example, since the inventory data is available for each month over these four years, the resulting time series would contain 12 • 4 = 48 observations.
An analysis of seasonal variation is often a crucial step in planning sales and production. Just because your sales drop from one month to the next does not necessarily mean that it is time to panic. If a review of past observations indicates that sales always drop between these two months, then quite likely there is no cause for concern.
Time Series
A time series represents a variable observed across time. The time increment can be years or months. The times series for the summer historical inventory data decreases steadily during the months of March, June, July, September, November, and December. The long-term movement in the time series is called a trend. These values exhibit a definite decreasing trend. The purpose of time series analysis is to describe a particular data set by estimating the various components that make up this times series.
Conclusion
In conclusion, when each index number uses the same year, the resulting set of values is an...
Introduction
Using the summer historical inventory data, I have used the seasonal variation which refers to periodic increases or decreases that occur within a calendar year in a time series. For the most part they are very predictable because they occur every year. This analysis will include converting data into an index and using the time series data from the index to forecast inventory for the fifth year.
Seasonal Variation
Seasonal variation is very predictable because they occur every year. When a time series consists of annual data, you cannot see what is going on within each year. Data reported in annual increments therefore cannot be used to examine seasonality. When time series data are monthly as it is in this case, seasonal variation may be evident. For example, since the inventory data is available for each month over these four years, the resulting time series would contain 12 • 4 = 48 observations.
An analysis of seasonal variation is often a crucial step in planning sales and production. Just because your sales drop from one month to the next does not necessarily mean that it is time to panic. If a review of past observations indicates that sales always drop between these two months, then quite likely there is no cause for concern.
Time Series
A time series represents a variable observed across time. The time increment can be years or months. The times series for the summer historical inventory data decreases steadily during the months of March, June, July, September, November, and December. The long-term movement in the time series is called a trend. These values exhibit a definite decreasing trend. The purpose of time series analysis is to describe a particular data set by estimating the various components that make up this times series.
Conclusion
In conclusion, when each index number uses the same year, the resulting set of values is an...