How can you present time series forecasting model results to stakeholders?

The first important thing to consider will be always understanding your audience and their background so that you can speak their language. Visualization and Presentation techniques will be used to translate the technicalities behind your model to Insights, Try to use as much as possible Insightful Numbers that trigger your audience to focus and pay attention to more details as you go on with your presentation. It is also important to present the Actual vs Predicted values and Model accuracy in your explanations.

O mais importante na demonstração de resultados de modelos de machine learning é traduzir os requisitos técnicos em resultados práticos do dia a dia. Explicar quais informações foram utilizadas dentro do modelo, quais transformações foram realizadas e quais resultados isso gera é crucial para um entendimento mútuo e criação de confiança entre o time funcional e técnico. Por fim, a apresentação de métricas de desempenho principais do modelo (RMSE / Curva ROC e AUC) e insights do mesmo servem para mostrar a aplicabilidade prática do modelo em questão e possíveis soluções para problemas de negócio.

I start with a summary which provides high level overview of key insights. I use a better visualizations to make complex information more accessible. Graphs, charts, and plots can help stakeholders understand patterns, trends, and the accuracy of model's predictions. I consider including - Actual vs. Predicted Time Series Plots, Confusion Matrix and Residual Analysis Plots.

Create a summary of the key highlights, the purpose time spent trends patterns can also be included. Use visual keys to show the data and predicted values. Bars, Heatmaps, and charts are the best visual representations. Including relevant matrics makes the presentation relatable and explains it in a non-technical way. Business impact, assumptions, and limitations, and recommendations should be the next topic of the agenda. The final finishing touches can be QnA and documentation so that anyone can go through at the later stages.

Start with a concise summary highlighting your time series model's objectives, data, and key findings. Use clear visualizations like the 'actual vs predicted' plot to demonstrate the model's accuracy. Include vital metrics like Mean Absolute Percentage Error (MAPE) or Coefficient of Determination (R²) for quantifying performance. A forecast plot showing future predictions is also beneficial. Ensure your presentation is clear and accessible, even for non-technical stakeholders. Conclude by discussing the practical implications of your findings, suggesting potential actions informed by the model's predictions. In this way your presentation is not just informative but actionable, bridging the gap between complex data and practical application.

Utilize ROC-AUC and precision-recall curves in addition to traditional metrics like MAE and RMSE when evaluating your model's performance on training and test datasets. This comprehensive approach not only captures the model's predictive accuracy but also assesses its ability to handle class imbalances and trade-offs between sensitivity and specificity. Incorporating these advanced metrics and visualizations provides a nuanced understanding of your model's performance, offering insights beyond conventional measures and enhancing your evaluation framework.

Demonstrating the performance of a time series model requires breaking the data into training and testing sets, maintaining chronological order. This ensures the model is evaluated on unseen data. Then we Calculate error metrics such as Mean Absolute Error and Root Mean Squared Error. These metrics quantify the average magnitude of the errors in predictions. Next we analyze the differences between observed and predicted values. Residuals should ideally be random and not show patterns over time. Finally use time-series cross-validation to assess model stability over different time periods. Effective demonstration of a time series model's performance requires combining statistical metrics, diagnostic checks, and contextual evaluation.

In my case we usually have two meetings, one technical and one with the stakeholders. In the second case, I always try to make the results understandable for all meeting participants and put together a friendly and not very technical presentation.

In the domain of time series forecasting, Alteryx stands out as a robust machine learning platform supporting MLOps. Leveraging historical data, especially in areas like demand vs. supply or spend vs. budgets, Alteryx facilitates comprehensive analyses. With its flexible capabilities, the platform enables the implementation of time series forecasts using diverse algorithms like ARIMA/ETS. The key lies in comparing forecasted outcomes with actual results, a pivotal step in evaluating algorithm performance. This hands-on approach empowers stakeholders to make informed decisions, guiding the selection of the most fitting algorithm for precise and effective forecasting in their specific context.

Show how well your time series forecasting model performs by sharing important metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). These numbers help measure how accurate your predictions are. Make things even clearer by using visuals like time series plots. These plots compare what your model predicted with what actually happened, making it easy for everyone to see how well your model understands trends and patterns in the data. This way, people can get a better idea of how good your model is at forecasting and where it might need some improvements.

There are many ways how the recommendations can be provided and what other added value your AI product/model is providing. Now, in demand forecasting particularly in supply chain domain the added value comes from not only from accurate forecasts but also how you are preventing your clients from overstocking which amounts to more savings (that’s the added value) and it gets catapulted when extended to multiple product lines. The data is best presented visually utilising graphs or dashboards. It makes information more digestible and decision-friendly

Craft actionable insights by leveraging SHAP values to interpret and communicate the model's feature importance effectively. This approach not only identifies key drivers behind predictions but also offers transparent explanations, addressing stakeholder queries about model interpretability and decision rationale. Providing concise and evidence-backed recommendations enhances the practical utility of your results, fostering a deeper understanding and facilitating informed decision-making for stakeholders.

The crucial stage of highlighting insights and recommendations necessitates a clear focus on important takeaways and practical ideas. 1. Begin by identifying major trends and patterns, then use visual aids to improve comprehension. 2. Emphasize actionable points that are connected with company objectives, giving stakeholders a clear relationship between the model's predictions and organizational goals. 3. To increase credibility, back up recommendations with evidence from model results, including metrics and real-world examples. 4. Demonstrate a commitment to continual improvement by outlining plans for model refinement and adaptation to changing business environments.

It's essential to tailor the presentation to the audience's knowledge level, clearly define the objectives and metrics, and provide a succinct overview of the data and chosen models. Use visual aids like charts and graphs to highlight key findings and compare the new model's performance against baselines or benchmarks. Always translate the technical results into business insights, emphasizing how the forecast can inform decision-making. Offer clear, actionable recommendations and utilize interactive tools or dashboards for a more engaging experience. Please always be prepared to discuss potential improvements and encourage stakeholder feedback to make sure the presentation is both informative and conducive to collaborative discussion.

Share valuable insights derived from your forecasting results. Identify patterns, trends, or anomalies discovered during the analysis. Translate these findings into actionable recommendations for stakeholders, emphasizing their potential impact on decision-making and strategy.

Use Line Charts to visually compare the predictions with the actuals over time. Try to present error plots to show the accuracy of the model. Use platforms like Tableau, Power BI, or custom-built dashboards to provide an interactive view of the forecasting results. Present different scenarios and their impact on future forecasts. Prepare a comprehensive written report that explains the method, key findings, and implications of the time series forecasting model. Use a language that is accessible to stakeholders who may not be familiar with the technical details. Clearly communicate any assumptions made during the modeling process. Also assess how changes in certain variables or parameters impact the forecast.

Leverage storytelling techniques in your presentation using tools like Plotly or Flourish to create interactive and engaging data visualizations. Weave a narrative that guides stakeholders through the model's journey, emphasizing key findings and actionable insights. This approach not only enhances comprehension but also captivates your audience, ensuring a more impactful and memorable presentation of your model results.

Generating insight is important but presentation matters the most. I believe presenting bad data in a good way is better than good data in a bad way. Also, tools and language need to be adjusted according to the audience. if the audience is internal technical teams, probably having a SQL queries-based dashboard would be good enough. However, if the audience is an executive team and non-technical people, then it needs to be tailored with the right tool. It is important they can interact with the data and drill down as well.

IMO for stakeholders, numbers do the talking instead of details on ML algorithms. From simple summary tables(pivots), to line charts, or heat maps using any tool say excel or Tableau or PowerBI etc. will help.

Once the preparation of data has been completed, there are many options for presenting the data. Familiar charts should be used such as line charts, tables and bar graphs. The final presentation depends on the delivery method. Interactive methods such as Power BI and Tableau are great but can be difficult to consume alongside commentary; I like to utilise these tools when presenting so that you can drill down live for any questions. If the insights are being sent via email, static methods such as PowerPoint or PDF with bullet points for key insights may be best.

This should be step #1. In my experience, a forecast is typically presented to a business stakeholder in a leadership position. What level of accuracy and detail do they really need. Start with the answer, "Here's what number you need to hit." From there, be prepared to give high-level explanations of assumptions and approach, and opportunities for more analytical minded folk when they ask. Include any pitfall assumptions to CYA.

Tailor your presentation by incorporating a concise executive summary at the beginning, catering to stakeholders with varying technical backgrounds. Utilize a conversational tone, avoiding jargon, and focus on translating complex concepts into practical implications. Additionally, engage your audience through interactive elements, such as live demonstrations or Q&A sessions, fostering a collaborative environment. This adaptive approach ensures your presentation resonates effectively with diverse stakeholders, promoting understanding and facilitating constructive discussions.

Understand stakeholders' background knowledge to determine the appropriate level of methodological detail to include. Consider stakeholders' strategic priorities and key decisions to focus presentations on the most relevant insights from the data. Tailor data visualizations, dashboards, and reporting formats to resonate with stakeholders based on their needs and preferred medium. Frame discussion of insights in the appropriate business context for stakeholders (e.g. operational efficiency, revenues, customer segmentation). Target explanation depth - executives may need high-level overviews while analysts want access to detailed technical appendices.

Marco algo como MUY IMPORTANTE a la hora de presentar los modelos: Definir el nivel de conocimiento técnico de quienes estarán recibiendo la información o de quienes serán los usuarios finales del modelo. Crear una presentación demasiado técnica para una audiencia que no conoce va a ocasionar perdidas de interés en nuestro trabajo. Cómo regla general y basica, se tienen que exponer los datos utilizados, los insights encontrados durante la exploración y la precisión del modelo final. A tener en cuenta: -Nivel de conocimiento de la audiencia -Data storytelling -Graficos funcionales y atractivos

We understood that our audience had different levels of technical expertise, so we customized our presentation accordingly. For those less familiar with technical details, we simplified complex concepts into easy-to-understand insights. We made sure to avoid using too much technical language and created visualizations that were clear and straightforward. On the other hand, for the more technically inclined audience, we went into specifics about the ARIMA model parameters and the details of time series differencing. This approach ensured that everyone, regardless of their technical background, could grasp and engage with the presentation.

Encourage a continuous feedback loop by implementing a feedback mechanism within your presentation, allowing stakeholders to share insights and suggestions seamlessly. Regularly conduct model performance reviews, considering evolving business dynamics and environmental factors. Embrace a growth mindset by exploring new skills, methodologies, or tools that align with advancements in time series forecasting. This proactive approach not only ensures the relevance and accuracy of your model but also fosters a culture of continuous improvement, driving long-term success in your forecasting endeavors.

Explain the Features you have considered in the dataset and their correlation with the ouput. explain the hypothesis you can achieve while designing the architecutre based on the features and type of features you have considered while designing the architecture.

Starting with an example related to real life scenario, will make it easier for the audience to understand the problem you are trying solve with your models and what your insights mean.

Expanding on the earlier sections, delve into the discussion of uncertainties associated with the time series forecasting model, exploring how it handles scalability and operational intricacies. Assess the model's robustness through a thorough evaluation and sensitivity analysis. Quantify the tangible business impact of the model's predictions, transparently communicate the assumptions guiding the forecasting process, and elucidate how the model seamlessly integrates with pre-existing systems. Conclude by outlining comprehensive plans for future iterations and the continual maintenance required to ensure the model's sustained effectiveness.

Models - and we're talking about good, modern predictive models that are producing non-trivial answers - are very difficult to understand. Some sense can be attempted in presentation and meetings, but the main point to focus on is DRIFT TESTING. Multiple models should be run in parallel in the real world and monitored to see which ones work best long te3rm. Retraining methods on new data should also be performed in parallel and compared. The fact is we're looking for an answer, and when explaining the "logic" is generally pointless, comfort must be provided with the most rigorous possible evaluation of different methods and their drifting characteristics on real world data. Leakage needs to be monitored as well.