Time2Vec: Enabling Forecasting with Limited Historical Data

Introduction

Time series data is the backbone of forecasting, from predicting sales, consumption, cash, people's journeys through an organization, the movement of financial instruments and more. This sequential, time-stamped information is crucial for understanding patterns within data that drive business decisions including trends, seasonality, and patterns. However, in today’s volatile and rapidly evolving business landscape, charting new directions with minimal data has become an unavoidable reality.

Whether it’s opening a new store, launching a new product, or adopting a +1 manufacturing strategy, businesses increasingly face scenarios where a lack of historical data makes forecasting future outcomes a challenge. And yet, the ability to make accurate predictions can mean the difference between success and costly mistakes.

Time-stamped information is crucial for understanding the patterns within data that drive business decisions including trends, seasonality, and patterns.

In this article, we'll explore some of the challenges inherent in time series data, such as data noise and scarcity, to enable enterprises to unlock the value for more accurate and timely decision-making.

The unique nature of time series data

To understand why forecasting with limited data is so challenging, we must first recognize what sets time series data apart. Unlike standard datasets, time series data has an inherent temporal structure. The order and intervals between data points carry crucial information, such as trends, seasonality, change points, or anomalies, that must be preserved and interpreted correctly. This temporal aspect is what makes generating insights from time series data complex, especially when historical data is scarce.

Traditional time series analysis methods, such as ARIMA (autoregressive integrated moving average) or exponential smoothing, have been effective for simple forecasting tasks, and excel when dealing with stable, predictable environments with extensive historical data.  However, they struggle in scenarios that reflect the practical realities in modern businesses.  

• Entering new markets or locations
• Launching new product lines or product categories
• Reaching new customer profiles in new markets
• Shifting manufacturing to more stable or cost-effective locations
• Exploring new sales channels
• Adapting to rapid market changes  

This is because traditional forecasting methods rely heavily on learning simplistic past trends and then extrapolating them into the future. These approaches struggle to account for sudden changes in market conditions, handle structural breaks in underlying systems, or draw insights from related but different time series. Overcoming these challenges requires more sophisticated techniques for modeling the intricate patterns in timeseries data.

The power of embeddings

To address these limitations, we turn to a powerful concept in machine learning: embeddings. Embeddings have revolutionized various fields of machine learning, particularly in the context of unstructured data including text, image and graphs. An embedding is a way to transform unstructured information into structured information, i.e. a collection of numbers or vectors in potentially high but fixed dimensional space. The remarkable property of such embeddings, when done right, is that despite such a transformation, they retain the key “informational relationship” present in unstructured space even in the transformed structured space.  

For example, Word2vec – a term introduced by Google engineers in 2013 offered a breakthrough approach to mapping words to vectors in a way that captured semantics of text. With this approach, one could see that “king – man + woman” resulted in a vector very close to “queen.” This breakthrough preserved the semantic properties of text (words, sentences, paragraphs) – not just the surface-level appearance – and was even found to preserve semantics across languages.

With the success of embeddings in language processing, ML teams soon began to seek out ways to apply this approach time series data.

Understanding Time2Vec

Time2Vec is a powerful technique that converts diverse time series data into standardized vector representations, essentially creating embeddings for time series data. Time2Vec enables data scientists to capture temporal patterns and relationships and reveals hidden relationships and patterns across various business aspects, regardless of data sources or limited historical information.

Time2Vec enables data scientists to capture temporal patterns and relationships, and reveals hidden relationships and patterns across various business aspects.

The Time2Vec technique provides several advantages:

• Dimensionality reduction: Complex time series can be represented in a more compact form
• Capture of temporal dynamics: Embeddings can encode information about trends, seasonality, and other temporal patterns
• Improved model performance: Many machine learning models perform better with vectorized inputs
• Transfer learning: Embeddings learned from one task can often be applied to related tasks

Ikigai’s implementation of Time2Vec

While the transformations or mapping of temporal data, i.e. Time2Vec technique, is not exclusive to Ikigai, Ikigai’s approach forTime2Vec is both innovative and powerful. Time2Vec is a core component of Ikigai’s aiCast^TM, serving as a critical tool for processing data in preparation for forecasting   tasks. Additionally, it’s available as a flexible building block within the broader Ikigai platform, allowing for customized applications across various use cases.

Ikigai’s advanced clustering and cohort analysis capabilities are fundamentally rooted in the Time2Vec technique. Traditionally, comparing the attributes of different products (for example, those with vastly different historical data lengths) presents a significant challenge, akin to comparing apples to oranges. However, Time2Vec overcomes this limitation by transforming diverse time series into a standard representation, enabling meaningful comparisons and insights across products with varying data depths.

This capability is particularly crucial in real-world business applications where data availability often varies significantly across products or services. Consider the scenario of forecasting sales for a newly launched product with only a quarter’s worth of data. By leveraging Time2Vec, Ikigai can identify clusters and cohorts of existing products that exhibit similar performance patterns, then apply the learned patterns of seasonality, trends, and other long-term behaviors to the new product.

As a result, we can make informed projections about the new product’s performance over a longer period of time. Additionally, this methodology also allows us to think about how you can find cohorts and clusters for things that don’t yet have history leveraging metadata, which would lend visibility into outcomes for completely novel scenarios such as new product introduction, or new store openings.

Conclusion

In an era where businesses must frequently navigate uncharted territories, the ability to make accurate forecasts with limited historical data is becoming a critical competitive advantage. Ikigai’s innovative implementation of Time2Vec addresses this pressing need by transforming timeseries data into rich, meaningful embeddings that capture complex temporal patterns.

If you’d like to learn more about how Ikigai can help your enterprise navigate uncertainty and optimize forecasting outcomes, visit www.ikigailabs.io and request a live demo.

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