What's Wrong With My Time Series?


Time series modeling sits at the core of critical business operations such as supply and demand forecasting and quick-response algorithms like fraud and anomaly detection. Small errors can be costly, so it’s important to know what to expect of different error sources. The trouble is that the usual approach of cross-validation doesn’t work for time series models. The reason is simple: time series data are autocorrelated so it’s not fair to treat all data points as independent and randomly select subsets for training and testing. In this post I’ll go through alternative strategies for understanding the sources and magnitude of error in time series.

Really, really important.

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