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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.
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TimeGPT is a transformer model for time series forecasting across fields like retail, electricity, finance, and IoT.
Demand forecasting is a critical component of effective supply chain management, but it's often more complex than it seems at first glance. Businesses must consider a wide range of factors to accurately predict demand and optimize their inventory levels. While there are many methods available for generating baseline demand forecasts, each has its own strengths and weaknesses.
Nousot, a company specializing in advanced analytics and supply chain management, has developed a unique demand forecasting framework that aims to address some of the common challenges businesses face. Their approach is built on three key principles:
Flexibility: Nousot combines deep learning techniques with traditional forecasting methods to ensure that they're always using the best tool for each specific time series and dataset.
Business alignment: Their framework incorporates "metric weighting" to generate forecasts that align with the strategic objectives of the business, whether that's maximizing fulfillment or running a leaner inventory.
Agility and experimentation: Nousot has optimized their package to run quickly, allowing for rapid experimentation with different modeling techniques, parameters, and external factors to continuously improve forecast accuracy.
Nousot's staged modeling approach begins by training a deep learning model on all available time series, which can capture trends across the entire dataset and provide strong baseline results. Then, the data is partitioned into common demand patterns so that deep learning models can learn the temporal patterns of each bucket. For series with weaker performance, they employ an intelligent model search over statistical methods to identify the best-performing technique for each specific case.
Statistical, machine learning, and deep learning forecasting approaches each have their own unique pros and cons
By combining models from all three forecast model approaches, Nousot has created a solution that is more accurate than any one individual approach on its own.
In addition to focusing on traditional error metrics like MAPE and Bias, Nousot works with businesses to identify the right mix of weighted metrics that reflect their unique goals and objectives. This ensures that the forecasts they generate are not only statistically accurate but also aligned with the company's strategic priorities.
Learn more about how metric weighting becomes an essential piece to effective demand forecasting solutions:
By adopting a metric weighting approach, businesses can create more nuanced, flexible, and business-aligned forecasts.
Ultimately, there is no one-size-fits-all solution to demand forecasting. That's why Nousot has put flexibility in their solution to account for the specific needs of each business.
By combining advanced analytics with a deep understanding of supply chain management, Nousot helps companies optimize their inventory levels, improve efficiency, and drive better business outcomes.
For businesses looking to take their demand forecasting to the next level, Nousot's innovative approach is definitely worth considering. Reach out to or to learn more about how Nousot's services can help your business optimize operations.