Time Series Analytics with No-Code AI

Start your journey to master Time Series Analytics with No Code AI. From retail sales to payment transactions to weather forecasting, time series analytics is one of the most useful tools to make sense of your historical data and use it to predict the future.

What you will learn

Impute missing values

When historical records don't exist, use episodic time series analysis to fill in the gaps in your data.
Perform what-if analysis

Adapt your forecasting model to create a range of different what-if scenarios with the synthetic control method.
Infuse machine learning

Leverage ML algorithms, including neural networks, for time series classification, to categorize your data or spot outliers.

Time Series Forecasting

Example: Demand Forecasting (Retail, E-Commerce)

Leverage historical sales data to predict future consumer demand and trends. Become proficient in various models, such as ARIMA or exponential smoothing, to confidentially select the best one for your business case.

Operations Planning

Example: Supply Planning (Manufacturing)

Use historical time series data to gain insights into operational inputs, and better understand the impact of changes in these inputs.

Time Series Classification

Example: Fraud Detection (Banking)

Historical patterns can help you predict the future behavior of your system, classify the inputs into categories and detect any anomalies in the data.

Course Outline

Course details

37 lessons
2 hours of video
3 projects
Certificate

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