Time Series Forecasting - Part 1

In this part, the general 

Some real-world application of TSA includes weather forecasting models, stock market predictions, signal processing, and control systems. Since TSA involves producing the set of information in a particular sequence, this makes it distinct from spatial and other analyses. We could predict the future using AR, MA, ARMA, and ARIMA models

To perform the time series analysis, we have to follow the following steps:

Collecting the data and cleaning it

Preparing Visualization with respect to time vs key feature

Observing the stationarity of the series

Developing charts to understand its nature.

Model building – AR, MA, ARMA and ARIMA

Extracting insights from prediction

With the help of “Time Series,” we can prepare numerous time-based analyses and results.

Forecasting: Predicting any value for the future.

Segmentation: Grouping similar items together.

Classification: Classifying a set of items into given classes.

Descriptive analysis: Analysis of a given dataset to find out what is there in it.

Intervention analysis: Effect of changing a given variable on the outcome.

What Are the Limitations of Time Series Analysis?

Time series has the below-mentioned limitations; we have to take care of those during our data analysis.

Similar to other models, the missing values are not supported by TSA

The data points must be linear in their relationship.

Data transformations are mandatory, so they are a little expensive.

Models mostly work on Uni-variate data.

While discussing TS data types, there are two major types – stationary and non-stationary.

Stationary: A dataset should follow the below thumb rules without having Trend, Seasonality, Cyclical, and Irregularity components of the time series.

The mean value of them should be completely constant in the data during the analysis.

The variance should be constant with respect to the time-frame

Covariance measures the relationship between two variables.

Non- Stationary: If either the mean-variance or covariance is changing with respect to time, the dataset is called non-stationary.

Converting Non-Stationary Into Stationary

Let’s discuss quickly how to convert non-stationary to stationary for effective time series modeling. There are three methods available for this conversion – detrending, differencing, and transformation.

Detrending

It involves removing the trend effects from the given dataset and showing only the differences in values from the trend. It always allows cyclical patterns to be identified.

Differencing

This is a simple transformation of the series into a new time series, which we use to remove the series dependence on time and stabilize the mean of the time series, so trend and seasonality are reduced during this transformation.

Yt= Yt – Yt-1

Yt=Value with time

Transformation

This includes three different methods they are Power Transform, Square Root, and Log Transfer. The most commonly used one is Log Transfer.