WHAT IS DATA FORECASTING?
Forecasting is a process of making predictions on future based on past and present data and most commonly by analysis of trends. Risk and Uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible. In some cases the data used to predict the variable of interest is itself forecasted. METHODS OF DATA FORECASTING. The appropriate forecasting methods depend largely on what data is available. If there is no data available, or if the data available are not relevant to the forecasts, then qualitative forecasting methods must be used. These methods are not purely guesswork—there are well-developed structured approaches to obtaining good forecasts without using historical data. Quantitative forecasting can be applied when two conditions are satisfied:
TIME SERIES OF DATA FORECASTING Time does play a key role in normal machine learning datasets. Predictions are made for new data when the actual outcome may not be known until some future date. The future is being predicted, but all prior observations are almost always treated equally. Perhaps with some very minor temporal dynamics to overcome the idea of “concept drift” such as only using the last year of observations rather than all data available. Data Forecasting is based on Time period. So, to analyze the data we need to have specific time intervals. Examples of time series data include:
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