How exactly the model will work:

<aside> 🤖 The KERN-Powered AI model does 2 things:

1. Predicts future fashion trends - by analyzing both historical and real-time market data including social media trends, online search patterns, and current sales data.
2. Recommends a range of quantity of units to produce - by evaluating current inventory, sales trends, and forecasted demand. </aside>

Implementing a time series analysis model —> inputting real data to help predict future values

Leveraging LSTM as a Foundation

The KERN model is built upon the foundational architecture of Long Short-Term Memory (LSTM) networks, enhancing their capabilities and addressing their limitations to better cater to the dynamic and complex nature of the fashion industry.

LSTM Architecture Unveiled:

• The ****core of an LSTM network harbors a memory cell🧠, which is like a storage unit that can remember information for a long time. This is what differentiates it from RNNs This capability is pivotal in capturing the intricate temporal dependencies inherent in fashion trends.

• LSTM consists of memory cells and three gates: input gate, forget gate and output gate.

  🚪The Input Gate: when data is being fed to the algorithm, it will first go through the input gate where they “sort” it and it'll decides if it is important to be stored in the memory cell

  🚪The Forget Gate: over time, the forget gate will decide to discard certain fate from the memory cell that is outdated / not relevant

  🚪 The Output Gate: when it's time to make a prediction or output, the output gate looks at the current state of the memory cell and produces the final result based on what's stored. There by extracting and processing data from the memory cell to generate predictions or insights.

  

Enhanced by KERN Model

Incorporating Knowledge Enhancement: While LSTM lays the groundwork, KERN amplifies its efficacy by integrating external knowledge bases, enhancing the model’s context-awareness and predictive accuracy.

Targeted Application in Fashion: Fashion trends are inherently non-linear and influenced by a myriad of factors. KERN, built atop LSTM, is tailored to unravel these complex, intertwined patterns, offering nuanced, actionable insights.

A Glimpse into Operational Dynamics

Trend Prediction:

1. Data Feeding: Real-time and historical data, encompassing sales & returns, social media trends, consumer preferences, fabric types, and the speed at which items get sold are fed into the KERN model.
2. Knowledge Integration: KERN enhances LSTM’s data processing with external knowledge, offering context-rich insights. This "knowledge" encompasses external data and insights like consumer behavior trends, cultural influences, and global fashion movements, enriching the model's predictive analytics.
3. Trend Forecasting: Leveraging LSTM’s temporal data handling and KERN’s knowledge enhancement to predict emerging fashion trends with heightened accuracy.

Quantity Recommendation:

1. Data Analysis: KERN analyzes intricate patterns within the data, informed by LSTM’s memory cells and enhanced by external knowledge integration.
2. Demand Forecasting: It projects future demand by deciphering complex, non-linear trends in historical and real-time data.
3. Optimal Production: Recommends production quantities aligned with forecasted demand, minimizing overproduction and associated waste.