The price of farm goods like rice, wheat, and corn can swing wildly. Why? Because they're swayed by seasons, supply and demand, policy shifts, and weather changes. These swings don't just affect farmers; they ripple through the entire economy.

To get a handle on these price swings, a new forecasting tool has been developed. It's a blend of two powerful techniques: TCN and XGBoost. TCN is great at spotting patterns over short and long periods. XGBoost, on the other hand, excels at understanding complex, nonlinear relationships.

The new model was tested using a massive dataset of 65, 750 historical price points. It was pitted against traditional models like ARIMA and LSTM, as well as other hybrids like Transformer-XGBoost and CNN-XGBoost. The results were clear: the TCN-XGBoost model came out on top.

It achieved an RMSE of 0. 26 and a MAPE of 5. 3%. That's a significant improvement over other models. Even during dramatic price swings, it held its own with an RMSE of 0. 28 and a MAPE of 6. 1%. This shows it can capture both the trends and the magnitude of price changes.

So, how does it work? TCN digs into the temporal features of the data, while XGBoost tackles the complex nonlinear relationships. Together, they offer a robust solution for predicting agricultural prices.

But here's a question to ponder: while this model is impressive, how well does it adapt to sudden, unexpected events? Like a major policy change or a natural disaster? That's something to think about as we move forward.

Guessing Farm Prices: A New Way to Predict Market Shifts

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