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      How to Use a Vessel Prediction Chart

      This line chart is an essential tool for predicting vessel arrivals by leveraging data from multiple sources. By understanding how to read the chart, users can make informed decisions and respond to changes in predictions.

       Introduction:

      image

      • This line chart visualizes vessel arrival predictions from multiple data sources over time.
      • It helps users track and compare vessel arrival predictions across different sources, including AIS, Terminal Data, and Carrier Data.

       

      a. Axes:

      X and y

      • X-Axis: Represents the prediction dates, showing the time when the data was recorded.
      • Y-Axis: Displays the vessel arrival prediction date for each data point.
      b. Lines:
      • Each colored line represents a different data source:
        • Blue (AIS): Automatic Identification System, a reliable source for real-time vessel data.
        • Green (Terminal Data): Terminal-provided API predictions.
        • Yellow (Carrier  Data): Live updates or other real-time predictions.
      c. Dots:
      • Every dot indicates a prediction, with two key pieces of information:
        1. Prediction Date (X-Axis) – The date when the prediction was made.
        2. Predicted Arrival Date (Y-Axis) – The date the vessel is predicted to arrive.

      2. How to Read the Line Chart:

      a. Understanding Predictions:


      • Follow the path of the dots and lines to observe how predictions evolve over time.
      • For instance:
        • Yellow Line (Carrier Data): Notice how predictions might change significantly between Aug 24 and Aug 30 as more real-time data is integrated.
        • Green and Blue Lines (API and AIS): Observe how predictions stabilize or change as new data points are added.
      b. Data Comparison:
      • You can compare predictions across data sources by looking at how close the colored lines are to each other.
      • A closer match between the lines indicates alignment in the prediction models. When lines diverge, it could highlight differences in data sources or prediction accuracy.

      3. Use Cases:

      a. Trend Analysis:

      • Track prediction trends to identify if the estimated arrival dates are becoming more accurate or deviating over time.
      b. Early Warnings:
      • Sudden changes in one data source may act as early warning signals, prompting further investigation into potential delays or early arrivals