{"id":1561,"date":"2022-02-16T16:41:02","date_gmt":"2022-02-16T16:41:02","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2022\/02\/16\/prepare-time-series-data-with-amazon-sagemaker-data-wrangler\/"},"modified":"2022-02-16T16:41:02","modified_gmt":"2022-02-16T16:41:02","slug":"prepare-time-series-data-with-amazon-sagemaker-data-wrangler","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2022\/02\/16\/prepare-time-series-data-with-amazon-sagemaker-data-wrangler\/","title":{"rendered":"Prepare time series data with Amazon SageMaker Data Wrangler"},"content":{"rendered":"
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Time series data is widely present in our lives. Stock prices, house prices, weather information, and sales data captured over time are just a few examples. As businesses increasingly look for new ways to gain meaningful insights from time-series data, the ability to visualize data and apply desired transformations are fundamental steps. However, time-series data possesses unique characteristics and nuances compared to other kinds of tabular data, and require special considerations. For example, standard tabular or cross-sectional data is collected at a specific point in time. In contrast, time series data is captured repeatedly over time, with each successive data point dependent on its past values.<\/p>\n

Because most time series analyses rely on the information gathered across a contiguous set of observations, missing data and inherent sparseness can reduce the accuracy of forecasts and introduce bias. Additionally, most time series analysis approaches rely on equal spacing between data points, in other words, periodicity. Therefore, the ability to fix data spacing irregularities is a critical prerequisite. Finally, time series analysis often requires the creation of additional features that can help explain the inherent relationship between input data and future predictions. All these factors differentiate time series projects from traditional machine learning (ML) scenarios and demand a distinct approach to its analysis.<\/p>\n

This post walks through how to use Amazon SageMaker Data Wrangler<\/a> to apply time series transformations and prepare your dataset for time series use cases.<\/p>\n

Use cases for Data Wrangler<\/h2>\n

Data Wrangler provides a no-code\/low-code solution to time series analysis with features to clean, transform, and prepare data faster. It also enables data scientists to prepare time series data in adherence to their forecasting model\u2019s input format requirements. The following are a few ways you can use these capabilities:<\/p>\n