Simple forecasting methods include naively using the last observation as the prediction or an average of prior observations.<\/p>\n
It is important to evaluate the performance of simple forecasting methods on univariate time series forecasting problems before using more sophisticated methods as their performance provides a lower-bound and point of comparison that can be used to determine of a model has skill or not for a given problem.<\/p>\n
Although simple, methods such as the naive and average forecast strategies can be tuned to a specific problem in terms of the choice of which prior observation to persist or how many prior observations to average. Often, tuning the hyperparameters of these simple strategies can provide a more robust and defensible lower bound on model performance, as well as surprising results that may inform the choice and configuration of more sophisticated methods.<\/p>\n
In this tutorial, you will discover how to develop a framework from scratch for grid searching simple naive and averaging strategies for time series forecasting with univariate data.<\/p>\n
After completing this tutorial, you will know:<\/p>\n
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- How to develop a framework for grid searching simple models from scratch using walk-forward validation.<\/li>\n
- How to grid search simple model hyperparameters for daily time series data for births.<\/li>\n
- How to grid search simple model hyperparameters for monthly time series data for shampoo sales, car sales, and temperature.<\/li>\n<\/ul>\n
Let\u2019s get started.<\/p>\n
![\"How](\"https:\/\/machinelearningmastery.com\/wp-content\/uploads\/2018\/10\/How-to-Grid-Search-Naive-Methods-for-Univariate-Time-Series-Forecasting.jpg\")
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