xDev Asia

Lag features, rolling statistics. Calendar features. Fourier features for seasonality. Target encoding. tsfresh auto features.

Time Series AI: Time Series Analysis & Prediction

Part 2: Machine Learning for Time Series

xdev.asia

Introduction

Lag features, rolling statistics. Calendar features. Fourier features for seasonality. Target encoding. tsfresh auto features.

1. Overview

Key concepts

Feature Engineering for Time Series is an important topic in the field of modern AI.

2. Architecture & Principles

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. Practice

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

Summary

Concepts	Key Takeaway
Architecture	Suitable for the problem
Training	Careful hyperparameter tuning
Evaluation	Multiple metrics

Lesson 4: Feature Engineering for Time Series