Duke University This web site contains notes and materials for an advanced elective course on statistical forecasting that is taught at the Fuqua School of Business, Duke University. It covers linear regression and time series forecasting models as well as general principles of thoughtful data analysis.
Seasonality Seasonality is a characteristic of a time series in which the data experiences regular and predictable changes which recur every calendar year.
Any predictable change or pattern in a time series that recurs or repeats over a one-year period can be said to be seasonal.
An index higher than 1 indicates that demand is higher than average; an index less than 1 indicates that the demand is less than the average. Cyclic behaviour[ edit ] The cyclic behaviour of data takes place when there are regular fluctuations in the data which usually last for an interval of at least two years, and when the length of the current cycle cannot be predetermined.
Cyclic behavior is not to be confused with seasonal behavior. Seasonal fluctuations follow a consistent pattern each year so the period is always known. As an example, during the Christmas period, inventories of stores tend to increase in order to prepare for Christmas shoppers.
As an example of cyclic behaviour, the population of a particular natural ecosystem will exhibit cyclic behaviour when the population increases as its natural food source decreases, and once the population is low, the food source will recover and the population will start to increase again.
Cyclic data cannot be accounted for using ordinary seasonal adjustment since it is not of fixed period.
Applications[ edit ] Forecasting has applications in a wide range of fields where estimates of future conditions are useful. Not everything can be forecasted reliably, if the factors that relate to what is being forecast are known and well understood and there is a significant amount of data that can be used very reliable forecasts can often be obtained.
If this is not the case or if the actual outcome is effected by the forecasts, the reliability of the forecasts can be significantly lower.
This attempts to reduce the energy needed to heat the building, thus reducing the emission of greenhouse gases. Forecasting is used in Customer Demand Planning in everyday business for manufacturing and distribution companies. While the veracity of predictions for actual stock returns are disputed through reference to the Efficient-market hypothesisforecasting of broad economic trends is common.
Such analysis is provided by both non-profit groups as well as by for-profit private institutions including brokerage houses  and consulting companies . Forecasting foreign exchange movements is typically achieved through a combination of chart and fundamental analysis.
An essential difference between chart analysis and fundamental economic analysis is that chartists study only the price action of a market, whereas fundamentalists attempt to look to the reasons behind the action.
An important, albeit often ignored aspect of forecasting, is the relationship it holds with planning. Forecasting can be described as predicting what the future will look like, whereas planning predicts what the future should look like. Selection of a method should be based on your objectives and your conditions data etc.
An example of a selection tree can be found here. Supply chain management - Forecasting can be used in supply chain management to ensure that the right product is at the right place at the right time. Accurate forecasting will help retailers reduce excess inventory and thus increase profit margin.
Studies have shown that extrapolations are the least accurate, while company earnings forecasts are the most reliable.Averaging and smoothing models Notes on forecasting with moving averages (pdf) Moving average and exponential smoothing models Slides on inflation and seasonal adjustment and Winters seasonal exponential smoothing.
Analyze Data and Text. Use feature selection to automatically identify variables with the greatest explanatory power.
Use exponential smoothing and Box-Jenkins (ARIMA) methods with seasonality to forecast time series, such as sales and inventory, from historical data. Chapter 15 Time Series Analysis and Forecasting Nevada Occupational Health Clinic is a privately owned medical clinic in Sparks, Nevada.
The clinic specializes in industrial medicine. Operating at the same site for. 1 Overview of Economic Forecasting Methods: Forecasting Techniques Causal Methods Time Series Methods Qualitative Methods Regression Analysis Multiple.
Excel Data Analysis Tool: Excel provides the Exponential Smoothing data analysis tool to simplify the calculations described above.
To use this tool for Example 1, select Data > Analysis|Data Analysis and choose Exponential Smoothing from the .
Jan 21, · Forecasting Methods - Regression Vs Exponential Smoothing Hello, fellow analysts and purveyors of the mundane, today's posting will focus on the advantages and disadvantages of forecasting using either the regression or exponential smoothing method, and why you would even find yourself needing to .