> For the complete documentation index, see [llms.txt](https://docs.chord.co/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.chord.co/model-your-data/predictive-models/revenue-forecasts.md). # Revenue forecasts ### Introduction Time series forecasts generate day-level forecasts for input series. Forecasts are generated a daily level in an ensemble time series forecasting system that blends weights via out-of-sample accuracy. Series are forecasted for the next 24 months. While series are generated on a daily level, they can be aggregated to the weekly or monthly level by aggregating over various time periods using the date dimension. Confidence intervals are based on variability of input series and do not include large system variability, such as macroeconomic changes. Note: Forecasts provided by Chord are best guess predictions based on the input data, are provided “as is” for analytics purposes, and do not constitute any guarantee explicit or implied of actualization of forecasts. Current forecasted series: * **Net Revenue**: total daily revenue for all customers. * **Reoccurring Revenue**: revenue generated by non-first purchase or "repeat" customers. * **First-Time Purchase Revenue**: revenue from first-time purchase customers. * **Number of First-Time Customers**: total number of new customers. ![](https://2938842758-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F5Oy2IAsu7IvAGAlWgC36%2Fuploads%2Fgit-blob-047ad1504e8e7b7c4ceab70338d67f2a087d610b%2Frevenue-forecasts-1.png?alt=media) ### Ensemble model and out of sample accuracies Time series models are generated using a range of time series modeling procedures, including ARIMA, Regression, and Theta models, and control for a range of seasonality indices and special events, such as holidays and common retailing events (Black Friday). These models are evaluated using out-of-sample (or “holdout”) periods that folded across short- and long- range holdouts into “best guess” forecasts. The variation for our confidence intervals is generated from the variation in the ranges of input ensemble models. Our models do not include exogenous variation outside of the series itself. This means that we are forecasting that series in isolation of outside influences. These exogenous influences include the macroeconomy and any other factors that could impact the series, including changes in a store’s product assortment or pricing. Similar to nearly all-time series forecasting models, these models should be interpreted as “all else equal” analyses; that is, everything outside the variation on this single series stays the same; these are the “best guess” estimates for the input series. ### Accuracy Accuracy is calculated via out-of-sample forecasts. 30, 60, and 90 day forecast are generated by holding the hold-out time periods from the data and forecasting from the new end date. The forecast of these models is compared with the actuals that happened during the hold-out period, along with the average mean absolute percentage error (MAPE). ### Horizon Forecasts are generated daily for the next two years. ### Eligibility Tenant stores are eligible if they have two years of history and over 1k transactions. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.chord.co/model-your-data/predictive-models/revenue-forecasts.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.