What is the difference between predicting and forecasting

The difference between forecasting and prediction is that forecasting is a branch of prediction. This is because forecasting concerns the gathering of past and present data in order to find out the outcomes for the future while predictions are not always about the future, ie, this is not bounded by any kind of time, like past or present. Forecasting is one of those methods which involve the proper analysis of a given data, and then, finding the outcome on the basis of that analysis, in the future.

For example, forecasting the impact of a meteor on earth based on its speed and structure. So, if we speak in general terms, it can be said that forecasting is the prediction of an outcome that will happen in the future based on the behavior and the facts in the past and the present.

It is a very beneficial method that is used in the field of economics and meteorology. Along with this, weather forecasting is also very popular among the people and hence, quite beneficial too. Since the model depends on previous observations, x i , this is called an autoregressive model. With these definitions, we can now appreciate why weather forecasting is not called weather prediction: weather forecasting predicts the whether in the future using temporal information.

For example, if there is a downpour at the moment, what is the likelihood that it will still rain in five minutes? Independent of all other features that influence the weather e. Imagine that we would perform weather prediction rather than forecasting. This would mean we were to ignore any temporal dimension and just consider the other physical features that influence the weather.

Imagine that is still raining outside. Oddly the atmospheric pressure i quite high, which is associated with clear skies. So, when you are asking your prediction model to estimate whether it is currently raining, the model would probably respond that it is unlikely to rain due to the high atmospheric pressure. One of the challenges of forecasting is finding the number of previous events that should be considered when making predictions about the future.

This also depends on whether you are making about the immediate or the distant future. On the contrary, prediction can be applied anywhere as long as there is an expected future outcome.

Forecasting uses mathematical formulas and as a result, they are free from personal as well as intuition bias. On the other hand, predictions are in most cases subjective and fatalistic in nature. For example, if you are predicting the result between two teams, and then you happen to be a supporter of one team, there will be some bias.

But this is not the case for scientific methods since they have a way of eliminating bias and enhancing the accuracy of the forecast. For example, the World Bank uses economic trends, and the previous GDP values and other inputs to come up with a percentage value for a country economic growth. However, when doing prediction, since there is no data for processing, one can only say the economy of a given country will grow or not.

In most cases, predictions are based on arbitrary methods and experiences such as astrology, superstition, instincts etc. On the other hand, forecasts are done using scientific data that is analyzed scientifically to generate a model. This implies that a forecast might change if the trends used to derive the models change.

Finally, predictions are usually done at the instance or a customer level while forecasts are done at the aggregate level. This implies that when making a prediction needs to have a situation in hand which requires estimated future result. However, forecasts arise from analysis of data and they may take time to develop.

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Want to learn more? Machine Learning - machine learning is a branch of artificial intelligence ai where computers learn to act and adapt to new data without being programmed to do so. The computer is able to act independently of human interaction. Data Science - data science is the study of big data that seeks extract meaningful knowledge and insights from large amounts of complex data in various forms.

Data Mining - data mining is the process of discovering patterns in large data sets. Big Data - big data is another term for a data set that's too large or complex for traditional data-processing software. Predictive Analytics - Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns trends that could potentially predict future outcomes.

It doesn't tell you what will happen in the future, rather, what might happen. Descriptive Analytics - Descriptive analytics is a type of post-mortem analysis in that it looks at past performance.

It evaluates that performance by mining historical data to look for the reasons behind previous successes and failures. Prescriptive Analytics - prescriptive analytics is an area of business analytics dedicated to finding a potential best course of action for a given situation. Data Analytics - plain and simple, data analytics is the science of inspecting, cleansing, transforming, and modeling data in order to draw insights from raw information sources.

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