AI & Machine Learning Laboratory

Advanced predictive models for Ghana cocoa price forecasting and risk assessment

92.4%

Prediction Accuracy

1.5M+

Data Points

ML Models

99.9%

Uptime

Select Prediction Model

Choose from our suite of specialized ML models

LSTM Neural Network

Deep learning model for time-series forecasting

94% Accuracy 30-day forecast

XGBoost Ensemble

Gradient boosting for price direction prediction

91% Accuracy Fast training

Facebook Prophet

Seasonality and trend forecasting

89% Accuracy Seasonal patterns

SVM Classifier

Support Vector Machine for price movement

87% Accuracy Robust

Model Parameters

Forecast Horizon 30 days

7 days 90 days

Confidence Level 95%

80% 99%

Training Period 2 years

Feature Selection

Historical Prices Volatility Data Trading Volume Weather Data Macro Indicators Seasonal Patterns

Prediction Results

LSTM Neural Network Last trained: 2 hours ago

Predicted Price (30 days)

$4,125.50

+7.3%

Prediction Interval

[$3,980 - $4,270]

95% confidence

Model Accuracy

94.2%

Test set R²

Mean Absolute Error

$42.80

Average error

Model Insights

Bullish Signal Detected

Model predicts upward trend with 85% confidence based on recent price momentum.

Volatility Alert

Expected volatility increase detected. Consider hedging strategies.

Model Comparison

Performance metrics across different models

Model	Accuracy (R²)	MAE ($)	Training Time	Best For	Status
LSTM Neural Network	0.942	42.80	15 min	Long-term forecasting	Active
XGBoost	0.912	58.30	2 min	Price direction	Active
Facebook Prophet	0.887	72.10	5 min	Seasonal patterns	Active
SVM Classifier	0.865	89.50	10 min	Binary classification	Active
ARIMA	0.821	102.30	1 min	Short-term forecasts	Inactive

Model Training Studio

Train and customize models with your parameters

Train/Test Split

80% training, 20% testing

Training Epochs

50 epochs

Learning Rate

0.01

Training Progress

Ready

Loss -

Accuracy -

Epoch 0/50

Time 0s

Feature Importance Analysis

Which factors most influence cocoa price predictions?

Historical Price Trend

95%

Volatility Index

78%

Weather Patterns

65%

Exchange Rates

52%

Global Demand

48%

Model Deployment

Deploy trained models for real-time predictions

Cloud API

Deploy as REST API for integration with other systems

Real-time predictions
99.9% uptime SLA
Auto-scaling

Mobile App

Export model for mobile applications (TensorFlow Lite)

Offline predictions
Low latency
Small footprint

Dashboard Integration

Embed predictions in existing dashboards

JavaScript library
Real-time updates
Custom styling

Model Documentation

Technical details and implementation guidelines

LSTM Model Architecture

The Long Short-Term Memory neural network consists of:

Input Layer: 50 timesteps × 6 features
LSTM Layers: 2 layers with 128 units each
Dropout: 0.2 for regularization
Dense Layers: 64 and 32 units with ReLU activation
Output Layer: 1 unit (price prediction)

# Python implementation
model = Sequential([
    LSTM(128, return_sequences=True, input_shape=(50, 6)),
    Dropout(0.2),
    LSTM(128, return_sequences=False),
    Dropout(0.2),
    Dense(64, activation='relu'),
    Dense(32, activation='relu'),
    Dense(1)
])

API Endpoints

Available REST API endpoints for model inference:

POST

/api/v1/predict

Get price predictions

Example Request:

{
  "model": "lstm",
  "features": {
    "price": [3820, 3835, 3850, ...],
    "volatility": [0.25, 0.26, 0.24, ...],
    "volume": [12450, 11870, 13240, ...]
  },
  "horizon": 30
}