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Model templating allows for the export and import of model configurations in AutoTS. For domain-specific forecasting tasks this can significantly enhance the model search and optimization process.
Exploring how the use of genetic algorithms and continuous learning principles creates a system that can adapt to changing patterns over time as the dynamics of the forecast source data changes.
Developing a visual intuition for how changing metric weightings used by AutoTS can affect the dynamics of the forecast generated.
By adopting a metric weighting approach, businesses can create more nuanced, flexible, and business-aligned forecasts.
Part 2 of a exploratory series on the TimeGPT model from Nixtla. We explore Transformer models and grow our understanding in how they can be applied to time series forecasting.
Part 1 of an exploratory series on the TimeGPT model from Nixtla. We'll take a look at how the model works and how it changes the way we think about common forecasting problems.
TimeGPT is a transformer model for time series forecasting across fields like retail, electricity, finance, and IoT.
Navigating the U.S. Housing Market: Insights for Homebuyers
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One of the key features that sets AutoTS apart from other forecasting solutions is its model template system. This system allows for the export and import of model configurations, which can streamline the forecasting process in various scenarios.
Time series forecasting is a critical component of decision-making across various industries, from finance to healthcare, retail to energy. As our world becomes increasingly complex and rapidly changing, traditional forecasting methods often struggle to keep pace with shifting trends and unexpected disruptions. This has led to the development of more capable approaches, such as
One of the most unique features of the forecasting package AutoTS is the ability to definite a metric weighting object that controls how the algorithm chooses an optimal model.
Demand forecasting is a critical process for businesses, particularly in retail, where accurate predictions of product sales can significantly impact operations. However, creating forecasts that truly serve business needs goes beyond simply predicting numbers. This is where metric weighting comes into play.
In the previous post in this series, we covered some of the characteristics of global models for time series forecasting. By training on a varied set of time series data, the model uses the dynamics in those series to create predictions for out-of-sample data. More on that here:
Recently, I've been fascinated by a new paradigm in forecasting started by Nixtla and their TimeGPT model.
As you can tell from the name, they are continuing with the popular 'GPT' ending made famous by ChatGPT.
TimeGPT is an advanced generative pretrained transformer model specifically designed for time series forecasting. It caters to numerous domains such as retail, electricity, finance, and IoT with minimal coding required. TimeGPT stands out as the only turnkey foundation model for time series, operable via public APIs, upcoming Azure Studio integration, or on private infrastructure.
In an insightful post on Nousot's website, Alexandra Haefele describes how AI and advanced analytics are revolutionizing demand forecasting. Traditional human-based forecasting is being phased out in favor of more efficient, data-driven approaches. Tammy Waggoner and Bennjamin Myers shared their expertise in this transition at Nousot’s Data Connect forum.
They outlined a four-step framework for demand forecasting:
Generate a statistical forecast.
Provide external adjustments (e.g., economic forecasts for specific industries).
Make strategic adjustments (e.g., changes in the sales team).
Finalize a consensus forecast.
Adopting these steps can lead to substantial benefits:
Decreased Holding Costs: Improving inventory turns enhances efficiency and cash flow.
Maximized Supply Chain Returns: Better fulfillment relationships prevent stock shortages and optimize supply chain performance.
Time Savings through Automation: Automating routine tasks allows demand planners to focus on strategic planning.
As stated in the article, “The future of forecasting leverages AI and advanced analytics to optimize inventory levels for dramatic cost savings.”
For more detail on these insights, visit: https://www.nousot.com/resources/using-genai-to-completely-disrupt-traditional-platform-migrations-to-databricks-2/
AutoTS is a powerful and flexible Python package for time series forecasting. Unique to AutoTS, it not only includes a wide variety of forecasting models—ranging from naive and statistical to machine learning and deep learning—but also offers over 30 time series-specific transforms. This combination provides robust preprocessing capabilities and extends its flexibility in model implementation. Remarkably, AutoTS supports forecasting for multivariate outputs and probabilistic forecasts, making it versatile for diverse applications.
In 2023, AutoTS achieved a milestone by winning the M6 forecasting competition, demonstrating superior performance in stock market forecasting over 12 months. This victory underscores its capacity for high-accuracy forecasts and effective scalability, handling tens of thousands of input series seamlessly.
Moreover, AutoTS integrates with AutoML feature search through genetic algorithms. This automation finds the best models, preprocessing, and ensembling strategies, significantly enhancing its usability. The flagship ensemble types, horizontal and mosaic, ensure that each series gets the optimal model while preserving scalability.
For more information, visit https://winedarksea.github.io/AutoTS/build/html/source/intro.html#autots.
Deciding when to buy a home is a crucial financial decision, influenced by a variety of economic indicators and personal circumstances. This report aims to equip you with the necessary information regarding the current housing market, mortgage rate forecasts, economic indicators, and a rent vs. buy analysis to help you make a well-informed decision about purchasing a home now or delaying it for 6-12 months.