A time series can be considered an ordered sequence of values of a variable at equally spaced time intervals. To model such data, one can use time series analysis (TSA). TSA accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend, or seasonal variation) that should be accounted for.

TSA has several purposes:

  1. Descriptive: Identify patterns in correlated data, such as trends and seasonal variations.
  2. Explanation: These patterns may help in obtaining an understanding of the underlying forces and structure that produced the data.
  3. Forecasting: In modelling the data, one may obtain accurate predictions of future (short-term) trends.
  4. Intervention analysis: One can examine how (single) events have influenced the time series.
  5. Quality control: Deviations on the time series may indicate problems in the process reflected by the data.

TSA has many applications, including:

  • Economic Forecasting
  • Sales Forecasting
  • Budgetary Analysis
  • Stock Market Analysis
  • Yield Projections
  • Process and Quality Control
  • Inventory Studies
  • Workload Projections
  • Utility Studies
  • Census Analysis
  • Strategic Workforce Planning

AlgoBeans has a nice tutorial on implementing a simple TS model in Python. They explain and demonstrate how to deconstruct a time series into daily, weekly, monthly, and yearly trends, how to create a forecasting model, and how to validate such a model.

Analytics Vidhya hosts a more comprehensive tutorial on TSA in R. They elaborate on the concepts of a random walk and stationarity, and compare autoregressive and moving average models. They also provide some insight into the metrics one can use to assess TS models. This web-tutorial runs through TSA in R as well, showing how to perform seasonal adjustments on the data. Although the datasets they use have limited practical value (for businesses), the stepwise introduction of the different models and their modelling steps may come in handy for beginners. Finally, business-science.io has three amazing posts on how to implement time series in R following the tidyverse principles using the tidyquant package (Part 1; Part 2; Part 3; Part 4).

 

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