Time Series Forecasting Techniques in Python

Time Series is a collection of data points at constant time intervals. Time series is one of the important as well as lesser-known fields of machine learning. Time series forecasting is very important as many prediction problems have a time component. In time series forecasting we analyze the time-dependent data for predicting long-term and seasonality trends. Making predictions about the future is known as extrapolation in classical statistics. In this talk, we will discuss the basic concepts of time series forecasting, various time-series forecasting techniques such as Autoregression, Moving Average, ARMA, ARIMA, SARIMA, SARIMAX, VARMA, HWES, and Dickey-Fuller test, and a case study that demonstrates the working of time series forecasting techniques on a real-world problem. We will discuss how to perform time series forecasting in Python using Pandas and statsmodels packages.

Outline 1. Introduction to Time Series forecasting techniques and basic steps in forecasting (04 Minutes) 2. Time Series forecasting techniques (15 Minutes) 3. Case study for Time series forecasting (8 Minutes) 4. Summary and Questions (03 Minutes)

Gajendra Deshpande he/him

I hold M.Tech. in Computer Science and Engineering and PG Diploma in Cyber Law and Cyber Forensics from National Law School of India University, Bengaluru India. I have presented talks/posters/papers at prestigious conferences including JuliaCon, London, PyCon France, PyCon Hong Kong, PyCon Taiwan, COSCUP Taiwan, PyCon Africa, BuzzConf Argentina, EuroPython, PiterPy Russia, SciPy USA, SciPy India, NIT Goa, and IIT Gandhi Nagar. Worked as a Reviewer and Program Committee member for reputed International conferences including SciPy USA, SciPy Japan, JuliaCon, JupyterCon, PyData Global, and PyCon India, and publishers include Manning USA and Oxford Univesity Press. I am also a GitHub Certified Campus Advisor. I lead the PyData Belagavi chapter and the OWASP Belagavi chapter.