neural-networks-forecasting

用于预测的神经网络

你擅长将神经网络和深度学习应用于供应链预测。你的目标是构建复杂的深度学习模型（LSTM、GRU、Transformers），以捕捉需求数据中的复杂时间模式、季节性和非线性关系。

初步评估

1. 数据量：数据是否充足？（神经网络需要 1000+ 个样本）
2. 模式：是否存在复杂的非线性关系或长期依赖？
3. 特征：多变量还是单变量？
4. 预测范围：短期还是长期预测？
5. 资源：是否有可用于训练的 GPU？

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用于需求预测的 LSTM

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import matplotlib.pyplot as plt

class LSTMForecaster:
    """
    LSTM-based demand forecasting
    """
    
    def __init__(self, sequence_length=30, forecast_horizon=7):
        self.seq_len = sequence_length
        self.horizon = forecast_horizon
        self.model = None
    
    def build_model(self, n_features):
        """Build LSTM architecture"""
        
        model = keras.Sequential([
            # First LSTM layer
            layers.LSTM(128, return_sequences=True,
                       input_shape=(self.seq_len, n_features)),
            layers.Dropout(0.2),
            
            # Second LSTM layer  
            layers.LSTM(64, return_sequences=True),
            layers.Dropout(0.2),
            
            # Third LSTM layer
            layers.LSTM(32, return_sequences=False),
            layers.Dropout(0.2),
            
            # Output layer
            layers.Dense(32, activation='relu'),
            layers.Dense(self.horizon)
        ])
        
        model.compile(
            optimizer=keras.optimizers.Adam(learning_rate=0.001),
            loss='mse',
            metrics=['mae']
        )
        
        return model

---

用于多步预测的 Transformer

class TransformerForecaster:
    """
    Transformer with self-attention for forecasting
    """
    
    def build_model(self, seq_len, n_features, horizon):
        inputs = layers.Input(shape=(seq_len, n_features))
        
        # Positional encoding
        x = self.positional_encoding(inputs)
        
        # Multi-head attention
        attention_output = layers.MultiHeadAttention(
            num_heads=8,
            key_dim=64
        )(x, x)
        
        x = layers.Add()([x, attention_output])
        x = layers.LayerNormalization()(x)
        
        # Feed-forward
        ff = layers.Dense(256, activation='relu')(x)
        ff = layers.Dense(n_features)(ff)
        
        x = layers.Add()([x, ff])
        x = layers.LayerNormalization()(x)
        
        # Output
        x = layers.GlobalAveragePooling1D()(x)
        x = layers.Dense(128, activation='relu')(x)
        outputs = layers.Dense(horizon)(x)
        
        model = keras.Model(inputs, outputs)
        model.compile(optimizer='adam', loss='mse')
        
        return model

---

时间卷积网络（TCN）

class TCNForecaster:
    """
    TCN with dilated convolutions
    """
    
    def build_tcn_block(self, x, filters, kernel_size, dilation_rate):
        # Dilated causal convolution
        conv = layers.Conv1D(
            filters=filters,
            kernel_size=kernel_size,
            padding='causal',
            dilation_rate=dilation_rate,
            activation='relu'
        )(x)
        
        conv = layers.Dropout(0.2)(conv)
        
        # Residual connection
        if x.shape[-1] != filters:
            x = layers.Conv1D(filters, 1)(x)
        
        return layers.Add()([x, conv])

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神经网络集成

def ensemble_forecast(models, X_test):
    """
    Combine predictions from multiple NN models
    """
    
    predictions = []
    for model in models:
        pred = model.predict(X_test)
        predictions.append(pred)
    
    # Average ensemble
    ensemble_pred = np.mean(predictions, axis=0)
    
    return ensemble_pred

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工具和库

• TensorFlow/Keras：深度学习
• PyTorch：灵活的神经网络
• N-BEATS：专用预测神经网络
• DeepAR：概率预测

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相关技能

• demand-forecasting：传统方法
• ml-supply-chain：通用机器学习
• optimization-ml-hybrid：与优化结合