Time Series Data Analysis: Turn Time Series Pharma Data into Business Intelligence Dashboards
“Every Person Creates 1.7 MB of Data Per Second” It shows how data is being collected today with time as a feature variable. This data is then processed and transformed into a sequence with time as a reference called time series.
Nowadays, a lot of companies are creating millions of bytes of data each day but only some of them are using it for their business growth. This query is not about using data but regarding the awareness of how data can be used for business success. The time series data analysis provides a broad overview of time series data and analyzes how it can be molded into fruitful insights.
What is Time Series Data Analysis
Time series data analysis is a statistical technique used to understand the data patterns after a fixed interval of timestamps. The time series analysis provides an overview of data repeated with a continuous time interval.
Let’s take a scenario to predict the demand for a drug in a Seattle area of the United States and the pharma owner wants to get insights on the sale of a drug in various regions of Seattle. How many dozes were sold, and the age of people who bought in high quantity. All those questions can be answered by using time series data analysis.
Techniques of Time Series Data Analysis
The time series data analysis can be done using different machine learning and deep learning techniques. These time series analysis techniques can be selected by counting a number of factors such as patterns with data points, and more.
Trend Analysis: Identify the long-term movement of time series data to forecast future trends. For instance, power consumption becomes high during peak hours or working days.
Seasonal Analysis: Understand the seasonal patterns of a time series data due to factors like climate, holidays, or other seasonal changes.
ARIMA/SARIMA: Model the dependence of a time series on its past values, as well as its differences and moving averages to forecast future values.
Exponential Smoothing: Calculate a weighted average of past data to forecast future values, particularly useful for short-term forecasting in inventory management, finance, and supply chain management.
Prophet: A forecasting tool that uses a decomposable time series model to forecast future values, and is able to handle seasonality, holidays, and other factors that affect the data.
Neural Networks (NN): Model complex relationships between variables and forecast future values, using algorithms inspired by the functioning of the human brain.
Why You Should Use Odyx yHat for Your Time Series Data Analysis
Odyx yHat is a time series data analysis and forecasting tool designed specifically for non-tech executives to leverage companies with quick data analysis. It’s an all-in-one solution for stakeholders where they can import pharma data, perform exploratory data analysis, draw data insights, and make business decisions better than before.
This artificial intelligence and machine learning solution provide quick time series analysis with the best insights within minutes in the form of histograms, bar charts, and heat maps. See this amazing tool in action by connecting to Odyx yHat.
