{"id":493,"date":"2015-11-04T17:15:54","date_gmt":"2015-11-04T17:15:54","guid":{"rendered":"http:\/\/kusuaks7\/?p=98"},"modified":"2025-02-26T14:52:58","modified_gmt":"2025-02-26T14:52:58","slug":"does-time-matter-modelling-temporal-dynamics-for-better-predictions","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/does-time-matter-modelling-temporal-dynamics-for-better-predictions\/","title":{"rendered":"Does Time Matter? Modeling Temporal Dynamics for Better Predictions"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tThis post is based on a joint research project with\u00a0Pierangelo Rothenbuehler<\/a>, who is also a part of the Experfy community***\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tExpert or intelligent systems can take many forms. In customer relationship management they should mostly help to understand the context of a customer: How does she feel? What are her motivations? What is she looking for? This allows us to provide meaningful context-based content to the customer. While motivations and intentions cannot be observed without mental involvement of the subject under study, behavioral observations are rather easy.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAt least in the digital economy they are. Most off-the-shelf BI stacks will enable you to obtain dense behavioral observations of your customers in digital products. Can we infer motivations and intentions from this? Not really, but indirectly, by using predictive analytics and anticipating future behavior (e.g.,\u00a0a customer who is about to stop using your product is very likely to have low motivation for product use). Intelligent systems predicting churn have big value. For example, this previous Experfy blog post<\/a>\u00a0describes a case in the Telecom industry where it is very easy to switching providers. Using an expert system that predicts\u00a0churn, customer relationship management can target customers who\u00a0are likely to churn and give them incentives. These can be vouchers, discounts, gifts, or\u00a0offerings to move to another product inside the company\u2019s portfolio.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Methods for Binary Behavioral Prediction<\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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By binary behavioral prediction we mean predicting if a behavior will occur or not. This is a classification problem. A number of techniques are applied to\u00a0this – from the (most) common logistic regression over support vector machines to decision trees and neural networks. Whenever you face a prediction problem, time is inherently included in your model you want to predict a\u00a0future<\/em> value of a criterion using data available in the\u00a0present<\/em>. But in most cases your model and predictors do not address any sequential ordering. Temporal dynamics are not explicitly modeled –\u00a0different lags of the predictors are all treated as static data. However, there can be value in incorporating sequential information in your prediction models. A very simple way of doing so is conducting some feature engineering on the time series of the predictors: i.e., taking the slope, the deltas, or the standard deviation of your time series observations and including them as predictors. But of course, this is not where it stops. Other interesting approaches to sequence modeling include the following:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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