> For the complete documentation index, see [llms.txt](https://docs.graphite-note.com/graphite-note-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.graphite-note.com/graphite-note-documentation/demo-datasets/demo-datasets/car-sales.md).
# Car Sales
1\. If you want to use Graphite Note demo datasets click "Import DEMO Dataset".
2\. Select a dataset you want to use to create your advanced analytics model. In this case, we will select **Monthly Car Sales dataset** to create a "Timeseries Forecast" analysis on car sales data.
3\. Once selected, the demo dataset will load directly to your account. Dataset view will automatically open.
4\. Adjust your dataset options on the Settings tab. Click the Columns tab to view the list of available columns with their corresponding data types. Explore the dataset details on the Summary tab.
5\. To create a new model in the Graphite Note main menu, click on "Models".
6\. You will get list of available models. Click on "New Model" to create new one.
7\. Select the model type from our templates. In our case, we will select "Timeseries Forecast" by double clicking on its name.
8\. Select the dataset you want to use to produce the model. We will use "Demo-Monthly-Car-Sales.csv".
9\. Name your new model. We will call it **"Timeseries forecast on Demo-Monthly-Car-Sales".**
10\. Write description of the model and select a tag. If you want to, you can also create new tag from pop-up "Tags" window, that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. To set up Timeseries forecast analysis first you need to define the "Target Column". That is a numeric column from your dataset that you'd like to forecast. In the case of Timeseries on monthly car sales dataset target column is "Sales"
13\. If dataset includes multiple time series sequences, you can select field that will be used to uniquely identify each sequence. In the case of our demo dataset, we will not apply Sequence Identifier field since we have only "Sales" target column.
14\.
15\. Click "Next" to open "Time/Date Column" selection. Choose "Month" as date column.
16\. From additional options below, choose "Monthly" as time interval and define "Forecast Horizon". We will set up forecast horizon to 6 months in the future.
17\. Click "Next" to activate "Seasonality" options step. Here, you can define seasonality specifics of your forecast. If time interval is set to daily on the next step you will also have "Advanced options" available.
18\. Click "Run Scenario" to train your timeseries forecast.
19\. Wait for a few moments and Voilà! Your Timeseries forecast is trained. Click on the "Performance" tab to get insights and view the graph with original(historical) and predicted model data.
20\. Explore more details on "trend", "Seasonality" and "Details" tabs.
21\. If you want to turn your model into action click on "Predict" tab in the main model menu.
22\. You can produce your own Forecast analysis based on the existing training results by selecting Start and End date from drop down calendar and clicking on "Predict" button.
23\. Use your model often to predict future sales results. The more you use and retrain your model, the smarter it becomes!
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3\. Once selected, the demo dataset will load directly to your account. Dataset view will automatically open.
4\. Adjust your dataset options on the Settings tab. Click the Columns tab to view the list of available columns with their corresponding data types. Explore the dataset details on the Summary tab.
5\. To create a new model in the Graphite Note main menu, click on "Models".
6\. You will get list of available models. Click on "New Model" to create new one.
7\. Select the model type from our templates. In our case, we will select "Timeseries Forecast" by double clicking on its name.
8\. Select the dataset you want to use to produce the model. We will use "Demo-Monthly-Car-Sales.csv".
9\. Name your new model. We will call it **"Timeseries forecast on Demo-Monthly-Car-Sales".**
10\. Write description of the model and select a tag. If you want to, you can also create new tag from pop-up "Tags" window, that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. To set up Timeseries forecast analysis first you need to define the "Target Column". That is a numeric column from your dataset that you'd like to forecast. In the case of Timeseries on monthly car sales dataset target column is "Sales"
13\. If dataset includes multiple time series sequences, you can select field that will be used to uniquely identify each sequence. In the case of our demo dataset, we will not apply Sequence Identifier field since we have only "Sales" target column.
14\.
15\. Click "Next" to open "Time/Date Column" selection. Choose "Month" as date column.
16\. From additional options below, choose "Monthly" as time interval and define "Forecast Horizon". We will set up forecast horizon to 6 months in the future.
17\. Click "Next" to activate "Seasonality" options step. Here, you can define seasonality specifics of your forecast. If time interval is set to daily on the next step you will also have "Advanced options" available.
18\. Click "Run Scenario" to train your timeseries forecast.
19\. Wait for a few moments and Voilà! Your Timeseries forecast is trained. Click on the "Performance" tab to get insights and view the graph with original(historical) and predicted model data.
20\. Explore more details on "trend", "Seasonality" and "Details" tabs.
21\. If you want to turn your model into action click on "Predict" tab in the main model menu.
22\. You can produce your own Forecast analysis based on the existing training results by selecting Start and End date from drop down calendar and clicking on "Predict" button.
23\. Use your model often to predict future sales results. The more you use and retrain your model, the smarter it becomes!
