The Irregular And Extended Time Series Forecasting Problem Mới Update

Irregular variation in time series refers to random or unpredictable fluctuations that occur over time and are not related to any systematic factors or trends. An example of irregular variation in time series might be unexpected spikes or dips in a company’s stock prices due to unforeseen events, such as natural disasters or unexpected changes in government policy.

For instance, if you are tracking the sales of ice cream in a city, you might see a sudden and unpredictable drop in sales on a particular day due to rain or cold weather. Similarly, if you are tracking the number of visitors to a website, you might see an unexpected surge in traffic on a particular day due to a viral social media post or news article.

Irregular variation is often difficult to predict or model, but it can be important to account for in time series analysis to avoid misinterpreting data or making inaccurate forecasts.

A time series forecasting problem involves predicting future values of a variable based on its historical values over time. Therefore, the following example represents a time series forecasting problem:

• Predicting the daily stock prices of a particular company for the next month based on its historical stock prices for the past year.

Other examples that represent time series forecasting problems include:

• Forecasting the monthly revenue of a retail store for the next year based on its monthly sales data for the past two years.
• Predicting the monthly energy consumption of a household for the next six months based on its past energy consumption data for the past year.
• Forecasting the hourly temperature readings for the next day in a particular city based on its historical temperature data for the past week.