 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.

Load and clean the data

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”