Introduction to Time-Series Analysis in Simple Words…!!

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This story revolves around Time Series Analysis, as the name suggests. A time series is a set of data points collected over a period of time. The term “time series analysis” refers to the process of analyzing time series data using a variety of statistical methods and techniques.

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A collection of data points or measurements collected at different or normal time intervals is referred to as a time-series data. A time series is a set of data points collected at evenly spaced intervals over a period of time. The data points can be reported hourly, regular, weekly, yearly, quarterly, or annually.

The method of using a statistical model to estimate future values of a time-series based on past results is known as time-series forecasting.
The mathematical techniques for analyzing time series data are referred to as time series analysis. We can derive useful numbers, patterns, and other data characteristics using these techniques. Line charts are used to visualize time series data. As a result, time series analysis entails comprehending the inherent characteristics of time series data in order to generate useful and reliable forecasts.

Time series are used in statistics, economics, and industry. The regular closing value of a stock index such as the NASDAQ or Dow Jones is a common example of time series results. Sales and demand forecasting, weather forecasting, econometrics, signal processing, pattern recognition, and earthquake prediction are all examples of time series applications.

Different component's of a Time-Series:

1. Trend- Over a long period of time, the trend shows the general direction of the time series results. The direction of a trend can be upward, downward, or horizontal (stationary). If the slope of a time series increases or decreases, this is referred to as a trend.
2. Seasonality- In terms of timing, direction, and magnitude, the seasonality variable shows a pattern that repeats itself. An rise in water consumption due to hot weather conditions in the summer is one example. Seasonality is described as a distinct repetitive pattern between regular intervals that is caused by seasonal factors. It may be due to the calendar month, the day of the month, weekdays, or even the time of day.
3. Cyclical Component- There are patterns that do not repeat themselves over time. A cycle is a time series that exhibits ups and downs, booms and slums, and is most often seen in business cycles. These periods are not periodic, but rather occur over a period of 3 to 12 years, depending on the time series’ existence. Another factor to consider is the cyclic nature of the behavior. It occurs when the series’ rise and fall patterns do not follow a fixed calendar-based sequence. The words ‘cyclic’ and’ seasonal’ should not be used interchangeably. It is cyclic if the patterns do not have fixed calendar dependent frequencies. Since, unlike seasonal effects, cyclic effects are also affected by business and other socio-economic factors.
4. Irregular Variation- When trend and cyclical patterns are eliminated from time series results, irregular variations emerge. These changes are erratic, volatile, and may or may not be spontaneous.
5. ETS Decomposition- It is a technique for separating the various components of a time sequence. Error, Trend, and Seasonality are both acronyms for Error, Trend, and Seasonality.

Data types:

As previously mentioned, time series analysis is the statistical examination of time series results. The term “time series data” refers to data that is recorded over several time periods or intervals. There are three types of time series data:-

• Time series data-Time series data is the set of measurements of the values of a variable at various points in time.
• Cross-sectional data- This is data from one or more variables that was collected at the same time.
• Pooled data- This type of data is made up of both time series and cross-sectional data.

Terminology used in Time Series:

In time series, we should be familiar with a number of words and concepts. The following are some of them:-

1. Dependence: Dependence refers to the relationship between two observations of the same variable made at different times in the past.
2. Stationarity: This refers to the series’ mean value remaining constant over time. Stationarity is not reached if past results accumulate and the values rise towards infinity.
3. Differencing: Differencing is used to monitor the auto-correlations to render the sequence stationary. In some time series analyses, we do not need differencing, and over-differenced series may result in incorrect estimates.
4. Specification: It may include using time series models such as ARIMA models to evaluate the linear or non-linear relationships of dependent variables.
5. Exponential Smoothing: In time series analysis, exponential smoothing estimates the value of the next cycle based on the previous and present values. It entails averaging data in such a way that the non-systematic components of each case or observation cancel out. To estimate the short term, the exponential smoothing method is used.
6. Curve fitting: When data is in a non-linear relationship, curve fitting regression is used in time series analysis.
7. ARIMA: ARIMA is an acronym for Auto Regressive Integrated Moving Average.