Forecasting is both a fascinating subject to study and an important technique applied in industry, government, and academic settings. Example applications include demand and inventory planning, marketing strategy planning, capital budgeting, pricing, machine predictive maintenance, macroeconomic forecasting, and supply chain forecasting.Forecasting typically requires time series data, and time-series data is ubiquitous nowadays, both within and outside of the data science field: weekly initial unemployment claims, tick-level stock prices, weekly company sales, daily number of steps taken recorded by a wearable, machine performance measurements recorded by sensors, key performance indicators of business functions, just to name a few.
However, time series data differs from cross-sectional data in that time-series data has temporal dependence, and this dependence can be leveraged to forecast future values of the series. Some of the most important and commonly used data science techniques to analyze time-series data and make forecasts based on them are those developed in the field of statistics and machine learning. For this reason, time-series statistical and machine learning models should be included in any data scientists’ toolkit.
Unlike some of my prior presentations and tutorials that covered both statistical and neural network-based models for time series analysis, this talk will be introductory in nature and will focus on the discussion of a couple of workhorse statistical time series models that are frequently applied to solving time series forecasting problems.
Specifically, I will sketch the family Autoregressive Integrated Moving Average (ARIMA) models (with and without seasonal components) and the class of Vector Autoregressive (VAR) Models, including a discussion of the advantages and disadvantages when using each of these models in time series forecasting scenarios. Both real-world and simulated time series will be used to illustrate the application of these techniques in Python. Exploratory time series data analysis will also be included in the presentation.
Workshop Overview and Author Bio
The Fundamentals of Statistical Time Series Forecasting
Jeffrey Yau, PhD
Chief Data Scientist at AllianceBernstein