An Introduction to Time Series When I think about Time Series, the first thing that comes to my mind is forecasting the future with the data that we have. Sometimes, our model does not perform well, sometimes it can give good predictions.
What I find most interesting about time series is that it is applicable in statistics, mathematical finance, econometrics, and pattern processing for forecasting. Most of the biggest industry analyses are financial analysis, weather analysis, and healthcare analysis. Python, C++, JAVA, MATLAB, and R are widely used programming languages for time series data.
For example, Python and C++ can be used for implementing trading strategies.
But these languages do not contain a lot of statistical libraries, that is when R language comes into the picture. Nowadays, Artificial Intelligence combined with Machine Learning is often used with this time series data. Machine Learning data in matrix structured time series data in unstructured form so first we have to represent it using structured data which is time series embedding, after this, we can convert this into a matrix, and we can fit that into the model. In finance, time-series data can predict the return in the stock market along with the knowledge of market insights.
If we look at the stock returns data of any company for doing time series analysis, we have to confirm that the provided data is periodic (no missing values). For analyzing, we can go by one time series which is a univariate variable, then multiple variables (2,3,..) time series, after that we can attach the time series model to a machine learning model.
Good models in time series are ARIMA, GARCH, Exponential Smoothing, and LSTM. I want to give a short brief about the basic steps of practicality for forecasting in the time series. Firstly, we have to fetch the data and plot it. To see if there are trends that are visible by programming. We can see if there are any seasonal effects, cycles available with the current data. We have to check if the variations are time-dependent and if there are sharp changes in the behavior.
Are there any outliers that are present?
We have to see the autocorrelation function and partial autocorrelation function. After this step, we have to remove the trend, seasonal effects, and cycles so that we get the stationary residuals. With this, we will do residual analysis and choose the best model to plot the residuals for doing the forecasting.
In conclusion, the key distinction (and subsequently the relationship) between univariate time series analysis, multivariate time series analysis, and machine learning models is quite important to note since each model has precedence over unique domains.
An Introduction to Time Series
Written by Krutarth Haveliwala
