Leverage Deep Learning for Predictive Analytics with RoadMap TrailBlazer

In the field of predictive analytics, deep learning is gaining attention alongside traditional statistical methods.

The complex, powerful models excel in identifying the underlying trends in data without the need for extensive manual data transformation, delivering accurate forecasts. However, the real-time application of deep learning models is often hindered by their high computational demands and time-consuming training process.

RoadMap TrailBlazer offers a solution with our pre-trained deep learning models, where users can leverage the power of deep learning effortlessly. Users can simply select from a list of both established models and promising newer architectures provided by @Nixtla’s NeuralForecast, all of which are popular in both academic research and real-world applications.

Users can also select the ‘All’ option to run all available models and automatically identify the best one based on a retrospective validation process. Upon completion, the best-performing model and its results are displayed directly within an Excel sheet for easy access and analysis.

Furthermore, the ‘All’ option with detailed forecast enabled displays forecast results from each model, allowing for comprehensive comparison and deeper insights into the predictive capabilities of each model.

Here is a demo of using our pre-trained deep learning models on wellness products. Using historical weekly sales data, our models have shown impressive accuracy, enhancing the decision-making processes.

To learn more about the technicalities behind this:

We have leveraged the technique of transfer learning, which involves pre-training models on vast time series datasets and later adapting them to user data. The set of our deep learning models is trained on the M4 dataset, which is one of the largest publicly available time series datasets encompassing various industries, as a foundational training resource.

Although not tailored toward specific industries, the broad dataset allows the models to learn a wide range of temporal trends. We trained the models on curated monthly and weekly datasets spanning from 2000 to 2015.

To further highlight the results, we conducted tests on 85 anonymized pharmaceutical products’ monthly sales data as well as 385 well-being products’ weekly sales data. In both tests, our deep learning models were able to improve predictive accuracy and reduce RMSE by 15% when benchmarked against seasonal na?ve models.

This is our first attempt at applying transfer learning in the field of time series forecasting highlighting areas for improvement such as enhancing model fine-tuning on user-specific data.

The RoadMap team will continue exploring and advancing the technologies to boost predictive power of TrailBlazer. Let us know if there is a specific deep learning model you'd like to see made available in TrailBlazer!

The ability to utilize deep learning models alongside the other 40+ advanced forecasting models TrailBlazer gives users the ability to quickly find the most optimal model for their specific data set and use case. A task that typically takes dedicated data science teams weeks to do, done in minutes with RoadMap TrailBlazer.

Thank you for reading and stay tuned for more updates!

To read more about TrailBlazer or to purchase a subscription - Visit the official product page here: labofdata.com/product/TrailBlazer

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