Who is involved in forecasting

Remember that forecasts are made in order to plan for the future. To do so, we have to decide what forecasts are actually needed. This is not as simple as it sounds. For example, do we need to forecast sales or demand? This is typically based on the projected demand for the goods and services offered. Investors utilize forecasting to determine if events affecting a company, such as sales expectations, will increase or decrease the price of shares in that company.

Forecasting also provides an important benchmark for firms, which need a long-term perspective of operations. Stock analysts use forecasting to extrapolate how trends, such as GDP or unemployment , will change in the coming quarter or year. The further out the forecast, the higher the chance that the estimate will be inaccurate. Finally, statisticians can utilize forecasting to analyze the potential impact of a change in business operations..

For instance, data may be collected regarding the impact of customer satisfaction by changing business hours or the productivity of employees upon changing certain work conditions. Forecasting addresses a problem or set of data. Economists make assumptions regarding the situation being analyzed that must be established before the variables of the forecasting are determined.

Based on the items determined, an appropriate data set is selected and used in the manipulation of information. The data is analyzed, and the forecast is determined. Finally, a verification period occurs where the forecast is compared to the actual results to establish a more accurate model for forecasting in the future. Stock analysts use various forecasting methods to determine how a stock's price will move in the future. They might look at revenue and compare it to economic indicators.

Changes to financial or statistical data are observed to determine the relationship between multiple variables. These relationships may be based on the passage of time or the occurrence of specific events.

Forecasting: Principles and Practice 2nd ed. Forecasting is about predicting the future as accurately as possible, given all of the information available, including historical data and knowledge of any future events that might impact the forecasts.

Goals are what you would like to have happen. Goals should be linked to forecasts and plans, but this does not always occur. Too often, goals are set without any plan for how to achieve them, and no forecasts for whether they are realistic. Quantitative forecasting models are used to forecast future data as a function of past data.

They are appropriate to use when past numerical data is available and when it is reasonable to assume that some of the patterns in the data are expected to continue into the future. These methods are usually applied to short- or intermediate-range decisions. Quantitative forecasting models are often judged against each other by comparing their accuracy performance measures.

We will elaborate on some of these forecasting methods and the accuracy measure in the following sections. Some forecasting methods try to identify the underlying factors that might influence the variable that is being forecast.

For example, including information about climate patterns might improve the ability of a model to predict umbrella sales. Forecasting models often take account of regular seasonal variations. In addition to climate, such variations can also be due to holidays and customs: for example, one might predict that sales of college football apparel will be higher during the football season than during the off-season. Several informal methods used in causal forecasting do not rely solely on the output of mathematical algorithms, but instead use the judgment of the forecaster.

Some forecasts take account of past relationships between variables: if one variable has, for example, been approximately linearly related to another for a long period of time, it may be appropriate to extrapolate such a relationship into the future, without necessarily understanding the reasons for the relationship.

One of the most famous causal models is regression analysis. In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. Trend — A trend is consistent upward or downward movement of the demand. Cycle — A cycle is a pattern in the data that tends to last more than one year in duration. Often, they are related to events such as interest rates, the political climate, consumer confidence or other market factors.

Seasonal — Many products have a seasonal pattern, generally predictable changes in demand that are recurring every year. Fashion products and sporting goods are heavily influenced by seasonality. Irregular variations — Often demand can be influenced by an event or series of events that are not expected to be repeated in the future.

Examples might include an extreme weather event, a strike at a college campus, or a power outage. Random variations — Random variations are the unexplained variations in demand that remain after all other factors are considered. Often this is referred to as noise. Time series methods use historical data as the basis of estimating future outcomes. A time series is a series of data points indexed or listed or graphed in time order.

Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus, it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Time series are very frequently plotted via line charts.

Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.

In the following, we will elaborate more on some of the simpler time-series methods and go over some numerical examples. In this case, the forecast for the next period is set at the actual demand for the previous period. This method of forecasting may often be used as a benchmark in order to evaluate and compare other forecast methods. For example, a manager may decide to use the demand values from the last four periods i.

Using the following table, calculate the forecast for period 5 based on a 3-period moving average. In practice, these weights need to be determined in a way to produce the most accurate forecast.

Note that if the sum of all the weights were not equal to 1, this number above had to be divided by the sum of all the weights to get the correct weighted moving average.

Exponential Smoothing This method uses a combination of the last actual demand and the last forecast to produce the forecast for the next period. There are a number of advantages to using this method. It can often result in a more accurate forecast.