Forecasting & Prediction

It is essential to differentiate between forecasting and prediction as those two words are used exchangeable without any consensus.  Forecasting is a sub-discipline of prediction as it is involved in predicting the future relying on time-series data. However, the prediction may further include estimation of the present outcome and may not concern the future (Döring, 2018). Specifically, when dealing with weather, it is commonly referred to as forecasting, while dealing with bank churn is mostly considered a prediction.

Prediction and forecasting leverage machine learning to deliver possible outcomes through learning from the past and historical data to provide trends and patterns. The algorithms are executed against data for training and learning purposes, performing hypothesis testing to build an optimized model (Love, 2002).  The process is referred to as supervised learning that depicts the learning of predictors features to produce the independent label (Kotsiantis, Zaharakis, & Pintelas, 2007). In prediction, regression and classifications can be used based on the investigated problem and the label types. Linear regression techniques are used to predict a continuous empirical variable against a univariate or multivariate independent variable. However, the model constructs pre-requisite several assumptions, such as linearity and homoscedasticity of the historical dataset to deliver an accurate outcome (Hellevik, 2009). An example of regression includes a prediction of salary, number of sales, or a product’s price. On the other hand, classification solves problems of a binary or a category type of the dependent label. It is commonly used to identify a status such as diagnosing a disease, a bank customer churn, or a spam email. 

Photo Credit: 2001: A SPACE ODYSSEY MOVIE

While prediction can be scientific using statistics and machine learning, one of the power tools is the imagination and inspiration that may accomplish those dreams into reality. Stanley Kubrick’s 2001: A Space Odyssey movie, broadcasted in 1968 and written by Arthur C. Clarke, used tablets by astronauts that looks identical to iPads (Larkin, 2019). Even Samsung claims that Apple did not invent the tablet and should not be a recognized patent (Westaway, 2011). The battle was due to Apple’s effort to block Samsung tablet sales, while the latter used the movie as evidence. The defense included a screenshot from the US court states’ film to prove that tablets are introduced way back then. Regardless of the winner of that battle, it demonstrated how imagination could play a significant role in shaping the future and make useful predictions. Nevertheless, it illustrates how imagination can be integrated with relational perspectives to promote organization creativity and position it as a pioneer (Thompson, 2018).

References

Döring , M. (2018). Prediction vs Forecasting, Data Science Blog.

Hellevik, O. (2009). Linear versus logistic regression when the dependent variable is a dichotomy. Quality & Quantity, 43(1), 59-74.

Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160(1), 3-24.

Larkin, B. (2019, October). 30 Predictions in History That Came True, BestLife. Retrieved from https://bestlifeonline.com/historical-predictions/

Love, B. C. (2002). Comparing supervised and unsupervised category learning. Psychonomic bulletin & review, 9(4), 829-835.

Thompson, N. A. (2018). Imagination and creativity in organizations. Organization Studies39(2-3), 229-250.

Westaway, L. (2011, August). Samsung says 2001: A Space Odyssey invested the tablet, not Apple, C/net. Retrieved from https://www.cnet.com/news/samsung-says-2001-a-space-odyssey-invented-the-tablet-not-apple/

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