Understanding time-series data and why it matters

Time-series data is one of the most valuable types of data used today by organizations across industries. Time-series data allows for a more in-depth understanding of changes, patterns, and trends over time. This enables organizations to gain insights into past behaviors and current states, as well as predict future values. The sequential tracking of data at precise time intervals enables both retrospective and prospective analysis that is extremely valuable for strategy, planning, and decision-making across industries.

Unlike static data types that provide an isolated snapshot, time-series data inherently captures dynamic relationships and correlations. For example, tracking hourly website clickstream data can reveal daily and seasonal traffic patterns that inform better resource planning to optimize infrastructure costs yet maintain performance during peak periods. Or a manufacturing facility can track defect rates from quality assurance checks multiple times a day to assess impacts of equipment upgrades or process changes and prevent potential failures through early anomaly detection. The time element provides context and connections that standalone data snapshots simply can’t offer. From predictive analytics to informed decisions and planning, time-series data helps organizations better understand their business environment, customers, and operations—making it one of the most versatile and valuable data types for data-driven strategies and digital transformation. The ability to analyze past sequences as well as predict future values based on emerging trends is what establishes time-series data as essential for success.

In this post, we discuss the nature of time-series data, its presence across different types of industries and various use cases it enables.

Overview of time-series data

Time-series data refers to data that is collected either over regular time intervals (metrics) or ones happen at irregular or unpredictable intervals (events). Unlike other data types that capture a single snapshot in time, time-series data tracks multiple data points over a specified time frame. As such, it provides a dynamic view of changes, patterns, and trends in the data rather than just a static snapshot, as illustrated in the following figure.

Some of the defining characteristics of time-series data include having a precise timestamp associated with each collected data point and a metric or value that needs to be tracked over time. For example, a manufacturing sensor may record temperature values from a machine component every minute, creating time-series data, a retail chain may track daily sales for each product category over many years, or a streaming service measure buffering times on peak hours across the world. In each case, timestamped data is captured over long time horizons.

The sequencing of time-series data also establishes important relationships between prior data points and ones that are captured later. Analysts utilize time ordering and sequencing to identify short- and long-term trends as well as seasonal, cyclical, or repeating patterns that may exist within the data. Time-series techniques also facilitate forecasting of future values based on observed historical patterns and fluctuations. In summary, this dynamic view and analytical value derived from tracking sequential data points over time is what defines time-series data. The ability to extract insights enables informed decisions and proactive responses to emerging trends is what makes it such an important type of data for many applications and industries.

Predictive analytics and anomaly detection with time-series data

Time-series data enables several important analytic use cases that bring business value. In this section, we discuss two particularly valuable time-series analysis use cases: predictive analytics and anomaly detection.

One of the prime capabilities time-series data unlocks is predictive modeling and forecasting. Historical patterns and trends mean analysts can utilize statistical, machine learning (ML), and artificial intelligence (AI) techniques to make data-driven predictions about future values and behaviors. For a retailer, this may involve projecting future sales by product line based on correlations with past sales data, pricing changes, and external factors like holidays. Manufacturers can predict spare part and resource demand based on sensor telemetry tracking wear rates of equipment components. The predictive insights from time-series analytics enable planning, logistics, and decision-making to be more proactive rather than reactive.

In addition to future projections, time-series data aids the identification of anomalies or abnormalities by comparing new data points against established baselines and ranges based on past sequences. Monitoring infrastructure performance metrics enables faster detection and alerts when metrics breach predetermined thresholds that indicate potential failures or degraded service quality. Industrial equipment sensors can detect subtle changes in vibration, temperature, or power consumption to identify signs of impending mechanical issues, enabling preventative maintenance. The ability to rapidly recognize anomalies enables critical monitoring use cases.

What all these use cases have in common is the need for an optimized data store that offers high ingestion throughput capabilities and efficient ways to automatically aggregate and access large amounts of data. As the volume and velocity of data continue to growth, traditional data storage solutions are struggling to keep pace. The sheer scale of data being generated by industrial equipment sensors, infrastructure performance metrics, and other IoT devices has rendered age-old solutions inadequate. The days of relying on relational databases or general-purpose NoSQL stores to handle the deluge of time-series data are behind us. Today, organizations require a dedicated store that can efficiently handle the high ingestion rates, automatic aggregation, and rapid querying of large datasets. A purpose-built time-series data store is essential to unlock the full potential of critical monitoring use cases, such as anomaly detection, predictive maintenance, and real-time analytics. By investing time in investigating a dedicated time-series data store, organizations can future-proof their data infrastructure, ensure scalability, and unlock new insights that drive business value.

Amazon Timestream offers two engine options to support your time-series needs. Timestream for LiveAnalytics is a fully managed serverless time-series engine built for scale, ideal for large scale real-time analytics and anomaly detection. Timestream for InfluxDB, based on the open-source versions of InfluxDB starting with 2.7, is optimized for high-speed, low-latency query workloads. Both options enable you to implement a range of time-series use cases, including predictive analytics and real-time monitoring, and are designed to help you extract valuable insights from your data.

Key use cases and applications

Time-series data and analysis provides crucial insights for a range of applications:

• Financial forecasting – Analysts use historical time-series of prices, trading volumes, and other financial metrics to identify trends and make predictions about future market movements. This supports trading, investment, portfolio monitoring, and risk management strategies.
• Demand planning – Retailers track time-series of past sales data to discern seasonal patterns, trends, and fluctuations in consumer demand. This helps optimize inventory orders and supply chain management.
• Infrastructure monitoring – Time-series data from sensors and monitoring tools allows analysis of performance metrics and utilization rates for critical IT, network, and data center infrastructure over time. This enables failure prediction and informed capacity planning.
• ML model efficiency – Time-series data allows you to identify and respond to potential risks, such as malicious or harmful interactions. By monitoring large language model (LLM) access patterns, you can develop a more informed approach to response denial, providing responsible AI development.
• Predictive maintenance – Industrial manufacturers collect real-time telemetry data from equipment and machinery to identify signs of declining performance. Early detection of potential failures enables proactive maintenance to prevent costly downtime.

The role of time-series data in an ML-driven world

Time-series data has become vitally important for many ML applications and models. This is driven by the fact that time-series data inherently captures patterns, sequences, and relationships that provide vital context. Many ML algorithms can use these temporal dynamics within the data to uncover complex insights and make more accurate predictions. Some examples include Autoregressive Integrated moving Averages, Exponential smoothing, Recurrent Neural Networks etc.

The aforementioned use cases like predictive analytics and anomaly detection are achieved thanks to ML models that have been trained with terabytes of time-series data that allows them to understand and predict contextual behavior. Sophisticated deep learning algorithms can account for complex sequential relationships and long-term temporal dependencies within massive historical datasets.

Additionally, timestamps in time-series data allow supervised ML models to understand appropriate sequences, attune to cyclic patterns, and distinguish truly anomalous data points, minimizing false positives. Temporal relationships provide essential context for accurate predictions and classifications. For example, patient health events over time can help predict disease progression risks.

Conclusion

In this post, we discussed the importance of time-series data and the use cases it unlocks, including predictive analytics, anomaly detection, and other key applications. Sequencing, long-term patterns, and temporal context within time-series datasets make them ideal training data for predictive, classification, and forecasting ML models across industries. Time-series data powers more accurate real-world algorithms.

If you are already using a time-series engine like InfluxDB, check out our Migration Guide to speed up your move to manage journey, or if you are just learning about time-series and to explore Timestream engines, keep learning by going to Amazon Timestream and choosing between our different engine options.

Victor Servin is a Sr Product Manager for the Amazon Timestream team at AWS, bringing over 18 years of experience leading product and engineering teams in the Telco industry and an additional 5 years of expertise in supporting startups with Product Led Growth strategies and scalable architectures, Victor’s data-driven approach is perfectly suited to drive the adoption of analytical products like Timestream. His extensive experience and commitment to customer success allows him to help customers to efficiently achieve their goals.