Exploring a milk production Time Series

Time series models are used in a wide range of applications, particularly for forecasting, which is the goal of this example, performed in four steps:

– Explore the characteristics of the time series data.
– Decompose the time series into trend, seasonal components, and remainder components.
– Apply time series models.
– Forecast the production for a 12 month period.

Part I of this mini-series explored the basic terms of time series analysis.

The dataset is the production amount of several diary products in California, month by month, for 18 years.
Our goal: forecast the next year production for one of those products: milk.

You can follow along with the associated notebook in GitHubContinue reading “Introduction to time series – Part II: an example”

Introduction to time series – Part I: the basics

A Time series is a data set collected through time.

What makes it different from other datasets that we used for regular regression problems are two things:

1. It is time dependent. So the basic assumption of a linear regression model that the observations are independent doesn’t hold in this case.
2. Most time series have some form of trend – either an increasing or decreasing trend – or some kind of seasonality pattern, i.e. variations specific to a particular time frame.

Basically, this means that the present is correlated with the past.
A value at time T is correlated with the value at T minus 1 but it may also correlated with the value at time T minus 2, maybe not quite as much as T minus 1.
And even at 20 times steps behind, we could still know something about the value of T because they’re still correlated, depending on which kind of time series it is.
And this obviously is not true with normal random data.

Time series are everywhere, for example in:

• Financial data (stocks, currency exchange rates, interest rates)
• Marketing (click-through rates for web advertising)
• Economics (sales and demand forecasts)
• Natural phenomenon (water flow, temperature, precipitation, wind speed, animal species abundance, heart rate)
• Demographic and population and so on.

What might you want to do with time series?

• Smoothing – extract an underlying signal (a trend) from a noise.
• Modelling – explain how the time series arose, for intervention.
• Forecasting – predict the values of the time series in the future.

We first see here which specific characteristics the Time Series (TS) have, and will then see in a second part a concrete example of TS analysis (smoothing + modelling + forecasting).

You can follow along with the associated notebook in GitHub. Continue reading “Introduction to time series – Part I: the basics”