- In this chapter you will discover a framework that you can use to quickly understand and frame your time series forecasting problem.
- A structured way of thinking about time series forecasting problems.
- A framework to uncover the characteristics of a given time series forecasting problem.
- A suite of specific questions, the answers to which will help to define your forecasting problem.
- What are the inputs and outputs for a forecast?
- What are the endogenous and exogenous variables?
- Are you working on a regression or classification predictive modeling problem?
- What are some alternate ways to frame your time series forecasting problem?
- Are the time series variables unstructured or structured?
- Are you working on a univariate or multivariate time series problem?
- Do you require a single-step or a multi-step forecast?
- Do you require a static or a dynamically updated model?
- Are your observations contiguous or discontiguous?
Time series forecasting involves developing and using a predictive model on data where there is an ordered relationship between observations
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Generally, a prediction problem involves using past observations to predict or forecast one or more possible future observations.
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Inputs: Historical data provided to the model in order to make a single forecast.
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Outputs: Prediction or forecast for a future time step beyond the data provided as input.
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Defining the inputs and outputs of the model forces you to think about what exactly is or may be required to make a forecast.
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you may not know whether one or multiple prior time steps are required to make a forecast.
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Endogenous: Input variables that are influenced by other variables in the system and on which the output variable depends.
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For example, the observation at time t is dependent upon the observation at t − 1; t − 1 may depend on t − 2, and so on.
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Exogenous: Input variables that are not influenced by other variables in the system and on which the output variable depends.
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Often, exogenous variables are ignored given the strong focus on the time series.
- Regression: Forecast a numerical quantity. fFr example a price, a count, a volume, and so on.
- Classification: Classify as one of two or more labels. For example hot, cold, up, down, and buy, sell are categories.
- It is useful to plot each variable in a time series and inspect the plot looking for possible patterns.
- Unstructured: No obvious systematic time-dependent pattern in a time series variable.
- Structured: Systematic time-dependent patterns in a time series variable (e.g. trend and/or seasonality).
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Univariate: One variable measured over time.
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Multivariate: Multiple variables measured over time.
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you may have multiple variables as input to the model and only be interested in predicting one of the variables as output. In this case, there is an assumption in the model that the multiple input variables aid and are required in predicting the single output variable.
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One-step: Forecast the next time step.
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Multi-step: Forecast more than one future time steps.
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The more time steps to be projected into the future, the more challenging the problem given the compounding nature of the uncertainty on each forecasted time step.
- Static: A forecast model is fit once and used to make predictions.
- Dynamic: A forecast model is fit on newly available data prior to each prediction.
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Contiguous: Observations are made uniform over time.
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Discontiguous: Observations are not uniform over time.
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The lack of uniformity of the observations may be caused by missing or corrupt values.
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It may also be a feature of the problem where observations are only made available sporadically or at increasingly or decreasingly spaced time intervals.
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In the case of non-uniform observations, specific data formatting may be required when fitting some models to make the observations uniform over time.