The Future With Time Series
Time Series is a sequence of fixed and ordered interval points in time. Time Series Data is a collection of observations; it could be collected yearly, monthly, weekly, and even hourly. It allows us to predict or forecast the future.
History repeats itself, where many cases that happened in the past are still happening now. Using this information, we can collect the benefits and prevent problems before they happen, or we can be well-prepared for something that will happen that needs more organization.
Time Series Forecasting
Time Series Forecasting is a method of using machine learning to predict the future based on previously observed time series. These forecasts are based on figuring out patterns.
Time Series Forecasting Methods or Patterns
1. Trend
Trend is the overall long-term direction of the series; in other words, trends are visible when there is an increase or decrease in the slope in the time series; it usually happens for a temporary period and then disappears. For example, some new songs were released and went on trend for a while.
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2. Seasonality
Seasonality occurs when there is a pattern in the data that repeats itself at regular periods, which occurs at regular intervals. It is related to seasonal nature or human behavior, such as tourism increases in the summers and springs.
3. Cycles
Cycles occur when a series follows an up-and down pattern that is not seasonal; Cycles can be of varying length, which is why it is more difficult to detect than seasonality.
ARIMA Model for Time Series Forecasting
Time series forecasting requires specific methodologies; we can not simply fit the time series data into a regular machine learning model. In timeseries data, several specialized models capture temporal structures. One of the most extensively used statistical methods for time series forecasting is the ARMA or the ARIMA model. ARIMA is an abbreviation for AutoRegressive Integrated Moving Average.
The AR and MA models are combined to create these models (The ARMA Models). The AR model is a model that is built on previous values in the series called lags, while the MA model is based on past errors in the series called error lags.
