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# Data Forecasting

When I was a kid I was very curious to know about weather forecasting ,how can someone predict if it will rainy or sunny next day .Now that I understand ,Weather forecasting is an important science. Accurate forecasting can help to save lives and minimise property damage.

How does forecasting work? What does it take to produce accurate, reliable and timely forecasts?

Meteorologists use a process called numerical weather prediction to create forecasts by inputting current conditions — which they call the “nowcast” — into computer models. The more current and accurate information available to these models, the better the forecast will be. Ground radar, weather balloons, aircraft, satellites, ocean buoys and more can provide three-dimensional observations that a model can use. This allows meteorologists to simulate what the atmosphere is currently doing and predict what will happen in the next few days or, for some models, hours.

But why are we talking about weather forecasting in data analytics as similar approach is done for forecasting in business.

Forecasting is a data science technique used to predict future happenings by considering historical data. The business uses forecasting for predicting any trend or future event. It helps in determining future demand for a product or a service too.

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.In Tableau, Exponential smoothing models with trend or seasonal components are effective when the measure to be forecast exhibits trend or seasonality over the period of time on which the forecast is based. Trend is a tendency in the data to increase or decrease over time. Seasonality is a repeating, predictable variation in value, such as an annual fluctuation in temperature relative to the season.

In general, the more data points you have in your time series, the better the resulting forecast will be. Having enough data is particularly important if you want to model seasonality, because the model is more complicated and requires more proof in the form of data to achieve a reasonable level of precision.

Lets look at Sample superstore dataset, we have used order date and sum of sales and we will forecast sales .

When we right click on the sheet it will give you option of forecast ,when you select that it gives you three options : Show Forecast,Forecast options and Forecast Description.In Forecast options you can select prediction interval from 90–99%.

The more higher percentage of prediction interval in our case we took 99% the more wider the prediction band will be.

Constraints of forecast: Last but not the least; the constraints of forecast are: you cannot add a forecast to a view if it contains any of the following: Table calculations Disaggregated measures Percent calculations Grand Totals or Subtotals Date values with aggregation set to Exact Date

Thus forecasting is a wonderful tool for future business growth