Sequence data is information where the order matters. Unlike regular data where each example is independent, sequences have temporal or positional dependencies.
Key Characteristics:
• Order matters - changing sequence changes meaning
• Variable length - sequences can have different sizes
• Context dependency - each element depends on previous ones
• Temporal dynamics - patterns evolve over time
💡 Why This Matters:
Traditional neural networks treat inputs independently. But what if today's weather depends on yesterday's? This is where sequences shine!
Common Types of Sequences
📝
Text & Language
Sentences, documents, conversations
📈
Time Series
Stock prices, weather, sensor data
🎵
Audio & Speech
Music, voice recordings, sound waves
🎬
Video
Movies, surveillance, action recognition
🧬
Biological
DNA sequences, protein structures
Sequence Examples in Action
📝 Text Sequence (Sentence)
The
→
cat
→
sat
→
on
→
mat
Each word depends on previous context for meaning
📈 Time Series (Stock Prices)
$100
→
$102
→
$98
→
$105
→
?
Tomorrow's price depends on historical trends and patterns
🧬 DNA Sequence
A
→
T
→
G
→
C
→
C
Gene expression depends on nucleotide sequence patterns