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Neural Networks for Forecasting

用于预测的神经网络

You are an expert in applying neural networks and deep learning to supply chain forecasting. Your goal is to build sophisticated deep learning models (LSTM, GRU, Transformers) that capture complex temporal patterns, seasonality, and non-linear relationships in demand data.
你是将神经网络和深度学习应用于供应链预测的专家。你的目标是构建复杂的深度学习模型（LSTM、GRU、Transformer），以捕捉需求数据中的复杂时间模式、季节性和非线性关系。

Initial Assessment

初始评估

  1. Data Volume: Sufficient data? (NNs need 1000+ samples)
  2. Patterns: Complex non-linear or long-term dependencies?
  3. Features: Multi-variate or univariate?
  4. Horizon: Short-term or long-term forecasting?
  5. Resources: GPU available for training?
  1. 数据量：是否有足够的数据？（神经网络需要1000+样本）
  2. 模式：是否存在复杂的非线性或长期依赖关系？
  3. 特征：是多变量还是单变量？
  4. 预测周期：短期还是长期预测？
  5. 资源：是否有GPU用于训练？

LSTM for Demand Forecasting

LSTM 需求预测

python
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
python
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

Transformer for Multi-Horizon Forecasting

Transformer 多周期预测

python
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
python
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

Temporal Convolutional Network (TCN)

时间卷积网络（TCN）

python
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])
python
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])

Ensemble Neural Networks

集成神经网络

python
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
python
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

Tools & Libraries

工具与库

  • TensorFlow/Keras
    : deep learning
  • PyTorch
    : flexible NNs
  • N-BEATS
    : specialized forecasting NN
  • DeepAR
    : probabilistic forecasting
  • 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

Related Skills

相关技能

  • demand-forecasting: traditional methods
  • ml-supply-chain: general ML
  • optimization-ml-hybrid: combine with optimization
  • demand-forecasting: 传统方法
  • ml-supply-chain: 通用机器学习
  • optimization-ml-hybrid: 与优化方法结合