# Time Series Analytics

> Optimized storage and analysis of time-ordered data with temporal operations.

- **Category**: Data Science
- **Subcategory**: Specialized
- **Canonical URL**: https://designpattern.fyi/patterns/time-series-analytics/

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## Description
**Context**: Time series data requires specialized handling for efficient storage, downsampling, and temporal queries. Standard databases are not optimal for time series workloads.


## Use Cases
IoT monitoring, financial data, application metrics, and any data with strong temporal characteristics.



## Implementation Example

```python
# Time Series Analytics Pattern import pandas as pd
# Time series resampling and aggregation ts_data = pd.read_csv("metrics.csv", parse_dates=["timestamp"]) ts_data = ts_data.set_index("timestamp")
# Downsample to hourly averages hourly_avg = ts_data.resample("H").mean()
# Rolling window calculations rolling_avg = ts_data["cpu_usage"].rolling(window="5min").mean()
# Time-based grouping daily_summary = ts_data.groupby(ts_data.index.date).agg({ "cpu_usage": ["mean", "max", "min"] })
```



## Trade-offs


### Advantages

- - Optimized for time-based queries

- - Efficient compression

- - Built-in downsampling

- - Temporal functions




### Considerations & Drawbacks

- - Specialized knowledge required

- - Limited to time series

- - Schema constraints

- - Vendor lock-in







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**Reference**: [Original Source](https://prometheus.io/docs/prometheus/latest/querying/basics/)