Times Adder

Overview

Times Adder is an API platform designed for the processing, analysis, and forecasting of time series data. Established in 2021, the service provides developers with tools to manage sequential data points indexed by time, which are common in fields such as financial market analysis, Internet of Things (IoT) sensor monitoring, and operational logging. The platform's core offerings include a Time Series Aggregation API, an Anomaly Detection API, and a Forecasting API, enabling users to derive insights from data collected over time.

The Time Series Aggregation API allows for the consolidation of data points into specified intervals, supporting various aggregation functions like sum, average, min, and max. This is applicable in scenarios requiring summarized views of high-frequency data, such as calculating average sensor readings per hour or daily trading volumes. The Anomaly Detection API identifies unusual patterns or outliers within time series datasets, which can be critical for fraud detection in financial transactions or identifying equipment malfunctions in industrial IoT deployments. The Forecasting API uses historical data to predict future values, supporting use cases like predicting stock prices, energy consumption, or component failure rates in predictive maintenance systems.

Times Adder is positioned for developers and technical buyers who require programmatic access to time series analytics without building custom algorithms. Its utility spans across industries, including finance for market trend analysis, manufacturing for monitoring equipment performance, and environmental science for tracking climate data. The platform offers SDKs for Python, JavaScript, and Go, facilitating integration into existing software stacks. The service's documentation includes runnable code examples, which can aid in the development process.

The platform's compliance with SOC 2 Type II attests to its security and operational controls, which is relevant for organizations handling sensitive data. While the service provides a free developer plan, its paid tiers are structured around request volume, catering to varying scales of data analysis. The emphasis on developer experience, including descriptive error responses and simplified integration via SDKs, aims to streamline the implementation of time series capabilities into applications. For complex data analysis tasks involving large datasets, the efficiency of time series databases like InfluxDB can be a relevant consideration, as described in their documentation on time series data fundamentals.

Key features

• Time Series Aggregation API: Consolidates data points over defined time intervals using functions such as sum, average, minimum, and maximum. Supports grouping and filtering operations for specific data subsets.
• Anomaly Detection API: Identifies statistical outliers or unusual patterns within time-indexed datasets. Configurable sensitivity levels to adapt to different data characteristics and use cases.
• Forecasting API: Predicts future data values based on historical time series patterns. Supports various forecasting models and allows for specifying prediction horizons.
• Real-time Data Processing: Designed to handle incoming data streams for immediate analysis and insight generation, suitable for dynamic environments like IoT or financial trading.
• Multi-language SDKs: Provides client libraries for Python, JavaScript, and Go, enabling developers to integrate the API into applications using familiar programming languages.
• Comprehensive Documentation: Offers API reference, tutorials, and runnable code examples to guide developers through integration and usage.
• SOC 2 Type II Compliance: Demonstrates adherence to security, availability, processing integrity, confidentiality, and privacy standards.

Pricing

Times Adder offers a tiered pricing model that includes a free developer plan and paid plans based on request volume. The following table summarizes the pricing as of May 2026.

For detailed and up-to-date pricing information, refer to the official Times Adder pricing page.

Common integrations

• Data Ingestion Systems: Integrates with platforms like Apache Kafka or AWS Kinesis for streaming real-time data into Times Adder for analysis.
• Data Visualization Tools: Connects with dashboarding solutions such as Grafana or Tableau to visualize aggregated data, anomalies, and forecasts.
• Cloud Platforms: Deploys within cloud environments like AWS, Google Cloud, or Azure to process data generated by cloud-native applications or IoT devices.
• Monitoring and Alerting Systems: Feeds anomaly detection results into operational monitoring tools like PagerDuty or Opsgenie to trigger alerts.
• Business Intelligence Tools: Exports processed time series data to BI platforms for deeper business insights and reporting.
• Machine Learning Workflows: Can be integrated into MLOps pipelines to provide pre-processed time series features for training custom machine learning models.

Alternatives

• InfluxData: Offers InfluxDB, a time series database and platform for storing, querying, and visualizing time series data.
• Grafana: An open-source platform for monitoring and observability, providing tools to visualize and analyze metrics, logs, and traces.
• Datadog: A monitoring and analytics platform for cloud-scale applications, providing end-to-end visibility across infrastructure and applications.
• Prometheus: An open-source monitoring system with a time series database, designed for reliability and scalability.
• AWS IoT Analytics: A fully-managed service that simplifies collecting, processing, and analyzing IoT data.

Getting started

To begin using Times Adder, you can sign up for a Developer Plan and obtain an API key. The Python SDK simplifies interactions with the API. The following example demonstrates how to send a series of data points for aggregation.

import os
from timesadder_sdk import TimesAdderClient
from datetime import datetime, timedelta

# Replace with your actual API key from Times Adder dashboard
API_KEY = os.environ.get("TIMESADDER_API_KEY", "YOUR_API_KEY")

client = TimesAdderClient(api_key=API_KEY)

def upload_and_aggregate_data():
    # Generate some sample time series data
    data_points = []
    start_time = datetime.now() - timedelta(hours=2)
    for i in range(120):
        timestamp = (start_time + timedelta(minutes=i)).isoformat() + "Z"
        value = 100 + (i % 10) * 0.5 + (i // 20) * 2 # Example varying value
        data_points.append({"timestamp": timestamp, "value": value})

    print(f"Uploading {len(data_points)} data points...")
    try:
        # Assuming a 'sensor_data' dataset ID
        upload_response = client.upload_data(dataset_id="sensor_data", points=data_points)
        print(f"Upload successful: {upload_response}")

        # Aggregate data for the last 2 hours, by 30-minute intervals, calculating the average
        aggregation_response = client.aggregate_data(
            dataset_id="sensor_data",
            start_time=(start_time).isoformat() + "Z",
            end_time=(datetime.now()).isoformat() + "Z",
            interval="30m",
            aggregation_function="avg"
        )
        print("Aggregation results:")
        for item in aggregation_response.get("aggregated_data", []):
            print(f"  Time: {item['timestamp']}, Average Value: {item['avg_value']:.2f}")

    except Exception as e:
        print(f"An error occurred: {e}")

if __name__ == "__main__":
    upload_and_aggregate_data()

This Python script initializes the Times Adder client, uploads a series of simulated sensor readings, and then requests an aggregation of that data. You would replace "YOUR_API_KEY" with your actual API key and potentially adjust the dataset_id to match your project's configuration. The Times Adder API reference provides further details on available endpoints and parameters.