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The choice is mostly about your specific task: what do you need/want to do? Many-to-one (single values) models have lower error, on average, since the quality of outputs decreases the more further in time you're trying to predict. Many-to-one (multiple values) sometimes is required by the task though. An alternative could be to employ a Many-to-one (single ...


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Using normal KFold cross-validation for time-series data will yield a highly optimistic error estimate since you are using data from the future to predict the past. The model just has to learn to interpolate, not to predict. Therefore you have to use time-wise CV: Furthermore, if your goal is to predict the future for a time-series you do not know the past ...


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Control charts are widely used still (whether people know it or not). However it is limited to cases where it is possible to define meaningful and stable targets and bounds for a metric. The classical example is in manufacturing, when the intent is to produce items which has a certain property at a certain value. Like each cereal box produced should be 510 ...


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That function can be decomposed into composite components: General trend Repeated frequencies (looks like there are 3 major cycles) Random noise A Fourier transform can decompose a function into its constituent frequencies.


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They all start from the same assumption: time series forecasting can't be treated as a regression/classification problem. It is time dependent, which means our target y at time t depends on what the value y was at t-1. Time series forecasting must take into account time dependency, but it doesn't have to be the only source of information. Many complex ...


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