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What is Forecasting in Data Mining?

Forecasting involves predicting what can happen in the future by considering past and present events and incidents. Forecasting is a decision-making tool that helps businesses cope with the uncertainty surrounding a business by carefully examining historical data and trends.

It can also be labelled as a planning tool allowing businesses to plot their upcoming moves and budgets accordingly. Companies use the forecasting tool with the hope that it will cover all the uncertainties that might occur. It is generally considered a good practice to indicate the degree of uncertainty attached to forecasts. One thing should always be kept in mind; the data must be up to date to forecast accurately.

It is essential for short-range and long-range planning. For example, the evening news gives the weather "forecast," not the weather "prediction." anyway, the terms forecast and prediction are often used interchangeably. Forecasting is based on many assumptions, such as:

  1. The past will repeat itself. It means that what has happened in the past will happen again.
  2. As the forecast horizon shortens, forecast accuracy increases. For example, a forecast for tomorrow will be more accurate than a forecast for next month, and a forecast for next year will be more accurate than a forecast for next year.
  3. Forecasting in the aggregate is more accurate than forecasting individual items. This means that a company can forecast total demand over its entire spectrum of products more accurately than it can forecast individual stock-keeping units (SKUs). For example, General Motors can more accurately forecast the total number of cars needed for next year than the total number of white Chevrolet Impalas with a certain option package.
  4. Forecasts are seldom accurate. Furthermore, forecasts are rarely accurate. While some are very close, few are "right on the money." Therefore, it is wise to offer a forecast "range." If one were to forecast demand of 100,000 units for the next month, it is extremely unlikely that demand would equal 100,000 exactly. However, a forecast of 90,000 to 110,000 would provide a much larger target for planning.

Characteristics of Forecasting

Below are some characteristics that are common to a good forecast:

What is Forecasting in Data Mining?
  • Accurate: Some degree of accuracy should be determined and stated so that a comparison can be made to alternative forecasts.
  • Reliable: The forecast method should consistently provide a good forecast if the user establishes confidence.
  • Timely: A certain amount of time is needed to respond to the forecast, so the forecasting horizon must allow for the time necessary to make changes.
  • Easy to use and understand: Users of the forecast must be confident and comfortable working with it.
  • Cost-effective: The cost of making the forecast should not outweigh the benefits obtained from the forecast.

Forecasting techniques range from simple to extremely complex. These techniques are usually classified as qualitative or quantitative.

How does Forecasting work?

Investors use Forecasting to decide if events affecting a business, such as sales expectations, might inflate or deflate the price of shares in that particular business. Forecasting methods also act as an important benchmark for businesses that require a long-term perspective of operations via key historical and non-stationary data.

Stock analysts use the Forecasting tool to extrapolate how trends, for example, GDP or unemployment, might alter in the coming financial quarter or year. Forecasting for a longer period decreases the chances of accurate Forecasting as many uncertain events can occur.

At long last, Statisticians can use Forecasting to analyze the likely impact of a change in business operations. For example, historical data may be collected for the impact of customer satisfaction by altering the business hours, or changes in certain work conditions might alter the company's overall productivity.

Forecasting methods address an issue or a set of data. Financial analysts make certain assumptions for the analyzed circumstances that must be established before the forecasting factors are determined. Because of the items decided, a suitable historical data set is chosen and used in the delusion of information.

The historical data in these forecasting techniques are broken down, and the forecast is determined accordingly. In the end, a verification period takes place where the forecast is contrasted with the actual results to set up a more precise future forecasting model.

Types of Forecasts

There are three major types of Forecasting, regardless of time horizon, that are used by organizations.

What is Forecasting in Data Mining?
  1. Economic forecasts address the business cycle. They predict housing starts, inflation rates, money supplies, and other indicators.
  2. Technological forecasts monitor rates of technological progress. This keeps organizations abreast of trends and can result in exciting new products. New products may require new facilities and equipment, which must be planned for in the appropriate time frame.
  3. Demand forecasts deal with the company's products and estimate consumer demand. These are also referred to as sales forecasts, which have multiple purposes. In addition to driving scheduling, production, and capacity, they are inputs to financial, personnel, and marketing plans.

Types of Forecasting Tools

Here are two types of forecasting tools, such as:

What is Forecasting in Data Mining?
  1. Qualitative Tools: These are based on a judgment we make based on experience and analysis of future trends. Due to the dependency of this tool on individual judgment, the forecast is affected by human biases.
  2. Quantitative Tools: These tools forecast data by analyzing past data. Further, it relies on statistical methods to make future predictions. These methods can be:
    • Time Series Analysis
    • Casual Methods

Methods of Forecasting

Stock analysts utilize different forecasting methods to determine how a stock price will change in the upcoming time. They look at income and compare it to the various economic indicators. Any financial or statistical data changes are minutely observed to decide the relationship between different variables. These relationships might be based on the passage of time or the development of certain events.

For example, a sales forecast might be based on the passage of time (the period of the upcoming 12 months) or the development of certain events (the purchase of a competitor's business). Below are the two basic methods of Forecasting. These two methods try to predict what will happen in the future.

What is Forecasting in Data Mining?
  1. Qualitative method of Forecasting: It is also known as the judgmental method. This method, for the most part, delivers subjective results. Qualitative forecasts are generally based on experts' or forecasters' judgments.
    The qualitative forecast is more often than not biased as they are comprised of an expert's knowledge, intuition, and experience. They are rarely based on data, which makes the process non-mathematical.
    One example of such a way of Forecasting that springs to mind are when a person forecasts the Cricket World Cup finals' outcome. It is, of course, based on personal motivation and interest. This method's biggest weakness is its inaccuracy and high failure rate. The failure rate is high because the forecast is not based on previous data and facts. Rather they are based on personal emotions and motivations.
  2. Quantitative Forecasting method: This one is quite the opposite of its other equivalent. It is a mathematical process that makes it more consistent and objective. The quantitative method does not believe in basing the results on the opinions and intuition of an individual. It instead uses a huge number of data and facts available in the past, and after analyzing these, it concludes. Hence, in this way, Quantitative Forecasting is done.
    For example, forecasting models based on time series, discounting, analysis of indicators that might be leading or lagging, and economic modelling are all classic examples of the Quantitative method of Forecasting.

Why is Forecasting Important?

Your business must also continually change to meet those needs. To make the most beneficial changes, you must predict what will happen in the future. This is where business forecasting comes into play. Forecasting is the practice of foretelling future events. Here are some reasons why Forecasting is essential for your business.

  1. Expanding into new markets
    Forecasting is necessary when venturing into new markets or setting up subsidiaries in new locations. Because it comes with so many risks and uncertainties, through Forecasting, you can determine your chances of succeeding in the target market.
  2. Investing your money wisely
    A continuous flow of money is the income of your company. So, you must put your money in areas that have the potential to generate excellent returns. And the best way to identify these areas is through Forecasting.
  3. Setting measurable short-term and long-term goals
    Goals offer a clear idea of what your business is striving to achieve. Creating short-term goals to attain long-term goals is an important aspect of any successful business. Forecasting helps you set both short-term and long-term goals. You can use three forms of Forecasting based on your business's size and type.
    • New businesses with little or no historical data often utilize a qualitative approach.
    • Businesses commonly use the quantitative approach, and they know the patterns that have been successful in the past.
    • Causal modelling analyzes different sets of data to establish if there is a causal relationship between them.
  4. Take advantage of real-time data
    Modern businesses obtain real-time consumer data from a wide array of sources. With sales forecasting models, you easily estimate future demand for a specific product or service, leveraging real-time consumer data.

Process of Forecasting

All the individuals who want to forecast need to follow the process listed below. By strictly adhering to this process, individuals can get accurate results. Some of the steps are as follows:

What is Forecasting in Data Mining?
  1. Develop the basis of ForecastingThe first step is to develop the basis of the investigation of the business's condition. The aim is also to identify the company's current position in the market.
  2. Estimate the business's future operations: The second step of Forecasting needs you to estimate the industry's future conditions that your business is set in. After you are done with the estimations, you need to project and analyze how your business will fare for a certain period in the future.
  3. Regulate the forecast: In the third step of the forecasting process, you need to look at different forecasts that were made in the past. After that, you need to compare those forecasts with the actual results that took place in the business. The differences between the previous and actual Forecasting results are analyzed. Then, the reasons for those deviations are considered in today's grand scheme. It is done to ensure that the current Forecasting can be done with as little deviation as possible.
  4. Review the process: All the forecasting steps mentioned above are thoroughly checked repeatedly to ensure no minute details have been missed. If some details are missed, then the refinements and modifications are made accordingly to optimize Forecasting.

Features of Forecasting

The following forecasting features can be identified in successful decision-making via key historical data, such as:

What is Forecasting in Data Mining?
  • Concerned with future events: Forecasting is concerned with future events. It is a systematic effort put into predicting the future. It can also be essentially called a technique of anticipation.
  • Based on past and present data: Forecasting, when following the Qualitative method, is based on opinions, intuition, and guesses. The Quantitative methods are based on facts, figures, time series, and other relevant data. All of the forecasting factors mentioned above contribute to making a forecast. To some extent, these factors reflect what happened with the company in the past and what will happen in the future.
  • Consideration of relevant facts: The forecasting technique considers all the factors affecting the business daily. This is a technique to catch hold of all the economic, social, and financial factors affecting the short- and long-term business goals revolving around different time series. Facts are crucial for qualitative as well as quantitative Forecasting.
  • Inference from known facts: Forecasting is a systematic process of knowing the future by making inferences from known facts. These facts are the data and information regarding the business activities that have taken place in the past. Hence, it is the analysis of past and present movements to predict future results.
  • Use of forecasting techniques: Most businesses worldwide use the quantitative forecasting method. As the method uses scientific, mathematical, and statistical techniques, the chances of deviation are minimal. Businesses use quantitative forecasting methods for budgeting and planning.
  • Element of guesswork: Personal observations certainly help individuals guess future events to a large extent. All the estimates of the futures are based on guesswork somewhat. People must already know that, along with guesswork, you need to analyze past and present circumstances.

How to choose the right Forecasting Technique

To choose the right forecasting technique, the individual need to keep the following points in mind:

  1. Desired forecast form: The forecast form can vary depending on the person. You can obtain a point estimate or a prediction interval. The form of the forecast influences the forecasting method that will be used.
  2. Time Pattern: The time frame is the total period for which the forecast is required. As the forecast time frame increases, the accuracy decreases along with it. We can say that the forecast's time frame and accuracy are inversely related.

Difference between Forecasting and Prediction

The terms 'forecasting' and 'prediction' are related to the future assumption. In business, estimation of the demand for the product in future is called Forecasting. It is not similar to prediction. Prediction is simply the guess of the manager. Forecast relies upon the scientific analysis of past data. But, prediction is dependent upon subjective considerations. The manager's job becomes quite easier when he has an accurate forecast.

Forecasting Predication
Forecasting is an estimation of future events which one can make by incorporating and casting forward data related to the past in a pre-determined and systematic manner. But, a prediction is an act in which a person declares or gives hints about some future events beforehand.
In Forecasting, the statement made is definitive. In the case of prediction, the statement is probabilistic.
Forecasting is scientific and unaffected by personal bias. However, prediction is subjective and may be influenced by personal bias. It is based on intuition.
Forecasting relies upon the 'throw ahead' concept. As per this concept, Forecasting requires patterns in data. But prediction depends on the 'saying ahead' concept. According to this concept, one can also make predictions from random data.
In Forecasting, error analysis is possible. Whereas in prediction, error analysis is not possible.
The results of Forecasting are replicable. But, the results of predictions are dependent upon unique representations
Because Forecasting uses a scientific approach, it is more accurate than prediction. Therefore, the probability of happening of the statement is higher in the case of Forecasting.
In business, Forecasting involves estimating the level of demand based on forces that generated the demand. However, prediction involves anticipating changes in the future that may or may not create demand.

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