Beyond Basic Trends with Flat.io: Customizing Your Time Series Forecasting

In the world of time series forecasting, having the right tools and techniques can make all the difference between accurate predictions and costly errors. One such tool is Flat.io, a powerful platform that allows users to build and customize their own trend functions beyond the three built-in options (piecewise linear, piecewise logistic growth, and flat). But how do you go about using this feature? And what are some common challenges you might encounter along the way?

To start, you'll need to download the source code from GitHub. From there, you can create a local branch and modify the trend function as desired. Once you've made your changes, you can install that local version of Flat.io. This level of customization allows you to tailor your forecasting model to the specific needs of your project.

However, it's important to note that using a custom trend function can come with some caveats. If you're working with a time series that doesn't have a constant trend, any trend you fit will be accompanied by high predictive uncertainty in the forecast. This is because the noise term will dominate the signal, making it difficult to accurately predict future values.

So, what kind of custom trends might you need? Some common scenarios where custom trends come into play include:

* Changes in seasonality between pre- and post-COVID

* Treating COVID-19 lockdowns as one-off holidays

* Prior scale for holidays and seasonality

* Seasonalities that depend on other factors

When working with seasonal data, it's also important to consider the locations of changepoints. These are points in time where a change occurs in the underlying pattern of your data. Prophet, another popular forecasting tool, offers automatic changepoint detection, which can be useful for identifying these shifts.

In addition to changepoints, you may also need to account for holiday effects and regressors in your model. Holidays, such as Christmas or Thanksgiving, can have a significant impact on time series data, while regressors allow you to incorporate external factors that might influence the pattern of your data.

By customizing your trend function with Flat.io, you can create more accurate forecasts that take into account the unique characteristics of your data. Whether you're dealing with changes in seasonality, holiday effects, or regressors, this level of control allows you to build a model that is tailored to your specific needs.

In conclusion, using custom trends with Flat.io requires a bit more effort than relying on built-in options, but the rewards can be significant. By downloading and modifying the source code, you can create a trend function that accurately captures the complexities of your data. So why settle for basic trends when you can have it all with Flat.io?

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