Tag: Time Series Forecasting

Time Series Forecasting is the process of analyzing time series data using statistics and modeling to make predictions and inform strategic decision-making. It’s not always an exact prediction, and likelihood of forecasts can vary wildly—especially when dealing with the commonly fluctuating variables in time series data as well as factors outside our control. However, forecasting insight about which outcomes are more likely—or less likely—to occur than other potential outcomes. Often, the more comprehensive the data we have, the more accurate the forecasts can be. While forecasting and “prediction” generally mean the same thing, there is a notable distinction. In some industries, forecasting might refer to data at a specific future point in time, while prediction refers to future data in general.
—Tableau, “Time Series Forecasting: Definition, Applications, and Examples”

When we associate a temporal or time component to [a] forecast, it becomes Time Series Forecasting and the data is called as Time Series Data. In statistical terms, time series forecasting is the process of analyzing the time series data using statistics and modeling to make predictions and informed strategic decisions. It falls under Quantitative Forecasting.

Examples of Time Series Forecasting are weather forecast over next week, forecasting the closing price of a stock each day etc.

To make close to accurate forecasts, we need to collect the time series data over a period, analyse the data and then build a model which will help is make the forecast. But for this process there are certain rules to be followed which help us achieve, close to accurate results.
—Analytics Vidhya, “Time Series Forecasting — A Complete Guide“

Computing & Technology / Articles

Metrics That Matter

Communications of the ACM, April 2019
By Benjamin Treynor Sloss, Shylaja Nukala, Vivek Rau

“One of the most important choices in offering a service is which service metrics to measure, and how to evaluate them. The difference between great, good, and poor metric and metric threshold choices is frequently the difference between a service that will surprise and delight its users with how well it works, one that will be acceptable for most users, and one that will actively drive away users—regardless of what the service actually offers. … What follows are the types of metrics the Google SRE team has adopted for Google services. These metrics are not particularly easy to implement, and they may require changes to a service to instrument properly. It has been our consistent experience at Google, however, that every service team that implements these metrics is happy afterward that it made the effort to do so.”