Which of the following forecasting methodologies is considered as a time series forecasting technique?

Forecasting is a technique that uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends.

Businesses utilize forecasting to determine how to allocate their budgets or plan for anticipated expenses for an upcoming period of time. This is typically based on the projected demand for the goods and services offered.

• Forecasting involves making predictions about the future.
• In finance, forecasting is used by companies to estimate earnings or other data for subsequent periods.
• Traders and analysts use forecasts in valuation models, to time trades, and to identify trends.
• Forecasts are often predicated on historical data.
• Because the future is uncertain, forecasts must often be revised, and actual results can vary greatly.

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.

Equity analysts use forecasting to extrapolate how trends, such as GDP or unemployment, will change in the coming quarter or year. 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. These analysts then come up with earnings estimates that are often aggregated into a consensus figure. If actual earnings announcements miss the estimates, it can have a large impact on a company's stock price.

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 when the forecast is compared to the actual results to establish a more accurate model for forecasting in the future.

The further out the forecast, the higher the chance that the estimate will be inaccurate.

In general, forecasting can be approached using qualitative techniques or quantitative ones. Quantitative methods of forecasting exclude expert opinions and utilize statistical data based on quantitative information. Quantitative forecasting models include time series methods, discounting, analysis of leading or lagging indicators, and econometric modeling that may try to ascertain causal links.

Qualitative forecasting models are useful in developing forecasts with a limited scope. These models are highly reliant on expert opinions and are most beneficial in the short term. Examples of qualitative forecasting models include interviews, on-site visits, market research, polls, and surveys that may apply the Delphi method (which relies on aggregated expert opinions).

Gathering data for qualitative analysis can sometimes be difficult or time-consuming. The CEOs of large companies are often too busy to take a phone call from a retail investor or show them around a facility. However, we can still sift through news reports and the text included in companies' filings to get a sense of managers' records, strategies, and philosophies.

A time series analysis looks at historical data and how various variables have interacted with one another in the past.  These statistical relationships are then extrapolated into the future to generate forecasts along with confidence intervals to understand the likelihood of the actual outcomes falling within that scope. As with all forecasting methods, success is not guaranteed.

The Box-Jenkins Model is a technique designed to forecast data ranges based on inputs from a specified time series. It forecasts data using three principles: autoregression, differencing, and moving averages. Another method, known as rescaled range analysis, can be used to detect and evaluate the amount of persistence, randomness, or mean reversion in time series data. The rescaled range can be used to extrapolate a future value or average for the data to see if a trend is stable or likely to reverse.

Most often, time series forecasts involve trend analysis, cyclical fluctuation analysis, and issues of seasonality.

Another quantitative approach is to look at cross-sectional data to identify links among variables—although identifying causation is tricky and can often be spurious. This is known as econometric analysis, which often employs regression models. Techniques such as the use of instrumental variables, if available, can help one make stronger causal claims.

For instance, an analyst might look at revenue and compare it to economic indicators such as inflation and unemployment. Changes to financial or statistical data are observed to determine the relationship between multiple variables. A sales forecast may thus be based on several inputs such as aggregate demand, interest rates, market share, and advertising budget (among others).

The right forecasting method will depend on the type and scope of the forecast. Qualitative methods are more time-consuming and costly but can make very accurate forecasts given a limited scope. For instance, they might be used to predict how well a company's new product launch might be received by the public.

For quicker analyses that can encompass a larger scope, quantitative methods are often more useful. Looking at big data sets, statistical software packages today can crunch the numbers in a matter of minutes or seconds. The larger the data set and more complex the analysis, however, the pricier it can be.

Thus, forecasters often make a sort of cost-benefit analysis to determine which method maximizes the chances of an accurate forecast in the most efficient way. Furthermore, combining techniques can be synergistic and improve the forecast's reliability.

Business forecasting tries to make informed guesses or predictions about the future state of certain business metrics such as sales growth or economy-wide predictions such as GDP growth in the next quarter. Business forecasting relies on both quantitative and qualitative techniques to improve accuracy. Managers use forecasting for internal purposes to make capital allocation decisions and determine whether to make acquisitions, expand, or divest. They also make forward-looking projections for public dissemination such as earnings guidance.

The biggest limitation of forecasting is that it involves the future, which is fundamentally unknowable today. As a result, forecasts can only be best-guesses. While there are several methods of improving the reliability of forecasts, the assumptions that go into the models, or the data that is inputted into them, has to be correct. Otherwise, the result will be garbage-in, garbage-out. Even if the data is good, forecasting often relies on historical data, which is not guaranteed to be valid into the future, as things can and do change over time. It is also impossible to correctly factor in unusual or one-off events like a crisis or disaster.

There are several forecasting methods that can be broadly segmented as either qualitative or quantitative. Within each category, there are several techniques at one's disposal. Under the qualitative methods, techniques may involve interviews, on-site visits, the Delphi method of pooling experts' opinions, focus groups, and text analysis of financial documents, news items, and so forth. Under quantitative methods, techniques generally employ statistical models that look at time series or cross-sectional data, such as econometric regression analysis or causal inference (when available).

Forecasts help managers, analysts, and investors make informed decisions about the future. Without good forecasts, many of us would be in the dark and resort to guesses or speculation. By using qualitative and quantitative data analysis, forecasters can get a better handle of what lies ahead. Businesses use forecasts and projections to inform managerial decisions and capital allocations. Analysts use forecasts to estimate corporate earnings for subsequent periods. Economists may make more macro-level forecasts as well, such as predicting GDP growth or changes to employment. However, since we cannot definitively know the future, and since forecasts often rely on historical data, their accuracy will always come with some room for error and in some cases may end up being way off.