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#9 Perform a Time Series Analysis Perfectly
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11 Jan
📊 Perform a Time Series Analysis Perfectly
Whats a time series analysis?
A time series is a data set that tracks a sample over time. Time series analysis involves statistical techniques to model and analyze time-ordered data points. It's used to understand underlying patterns such as trends, seasonality, and cycles in data that vary over time. By dissecting these patterns, analysts can forecast future values, which is invaluable in fields ranging from finance and economics to environmental science and healthcare.
Timeseries Example | Img Source: SpringBoard
How can time series analysis help?
Time series analysis provides a framework for companies to measure, track, and forecast the performance of various metrics over time, enabling more data-driven decision-making. Here are examples of how it can be applied across different company metrics:
Sales Revenue: Companies can use time series analysis to track sales revenue across daily, weekly, monthly, or yearly intervals. By identifying seasonal patterns, trends, and outliers in sales data, businesses can optimize their sales strategies, plan inventory, and forecast future sales more accurately.
Website Traffic: Digital marketers and website managers analyze time series data of website traffic to understand visitor behavior over time. This analysis can reveal the impact of marketing campaigns, seasonal variations in interest, and the effectiveness of SEO strategies.
Customer Demand: Retail and e-commerce companies can apply time series analysis to customer demand data to anticipate peak shopping periods, manage stock levels efficiently, and tailor promotions to when customers are most likely to make purchases.
Top Python libraries for Time Series Analysis in 2024
Statsmodels: This library includes classical statistical models and tests to conduct hypothesis tests and comprehensive data exploration such as ARIMA (Autoregressive Integrated Moving Average) and seasonal decomposition models
Scikit-learn: Known for its versatility in machine learning tasks by employing regression models, ensemble methods, and more, although it doesn't specialize in time series models.
Facebook Prophet: Prophet excels with daily observations that display strong seasonal effects, making it user-friendly and more flexible in handling missing data and trend changes.
a daily time series of the observed electricity demand in California src.
📰 Data Tools, Articles and Resources
Featured
Prophet: Since we’re covering time series this week, I thought of featuring one of the best ways to go about a time series analysis. Using Prophet, an open source library by Facebook.
Resources