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ARIMA(p,d,q) model. Seasonal ARIMA. Exponential Smoothing (ETS). Model selection: AIC, BIC. statsmodels implementation.

Time Series AI: Dự đoán & Phân tích Chuỗi Thời gian

Phần 1: Nền tảng Time Series Analysis

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Giới thiệu

ARIMA(p,d,q) model. Seasonal ARIMA. Exponential Smoothing (ETS). Model selection: AIC, BIC. statsmodels implementation.

1. Tổng quan

Khái niệm chính

Statistical Models — ARIMA, SARIMA & ETS là một chủ đề quan trọng trong lĩnh vực AI hiện đại.

2. Kiến trúc & Nguyên lý

Core Architecture

# Example implementation
import torch
import torch.nn as nn

class ExampleModel(nn.Module):
    def __init__(self, input_dim, output_dim):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(input_dim, 256),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, output_dim),
        )
    
    def forward(self, x):
        return self.net(x)

3. Thực hành

Setup

pip install torch transformers datasets

Training Pipeline

# Training loop
model = ExampleModel(input_dim=768, output_dim=10)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
criterion = nn.CrossEntropyLoss()

for epoch in range(10):
    for batch in train_loader:
        optimizer.zero_grad()
        outputs = model(batch["input"])
        loss = criterion(outputs, batch["label"])
        loss.backward()
        optimizer.step()

4. Best Practices

Aspect	Recommendation
Data	Quality over quantity
Model	Start simple, scale up
Training	Monitor loss curves
Evaluation	Use appropriate metrics

Tổng kết

Concept	Key Takeaway
Architecture	Phù hợp với bài toán
Training	Careful hyperparameter tuning
Evaluation	Multiple metrics

Bài 2: Statistical Models — ARIMA, SARIMA & ETS