All of the examples can be found in Jupyter notebook form here.

# Time Series Analysis

A time series is a sequence of data points, each associated with a time. In our example, we will work with a time series of daily temperatures in the city of Melbourne, Australia over a period of a few years. Let $x$ be the vector of the time series, and $x_i$ denote the temperature in Melbourne on day $i$. Here is a picture of the time series:

using Plots, Convex, ECOS, DelimitedFiles
aux(str) = joinpath(@__DIR__, "aux", str) # path to auxiliary files

n = size(temps, 1)
plot(1:n, temps[1:n], ylabel="Temperature (°C)", label="data", xlabel = "Time (days)", xticks=0:365:n)

We can quickly compute the mean of the time series to be $11.2$. If we were to always guess the mean as the temperature of Melbourne on a given day, the RMS error of our guesswork would be $4.1$. We'll try to lower this RMS error by coming up with better ways to model the temperature than guessing the mean.

A simple way to model this time series would be to find a smooth curve that approximates the yearly ups and downs. We can represent this model as a vector $s$ where $s_i$ denotes the temperature on the $i$-th day. To force this trend to repeat yearly, we simply want

$s_i = s_{i + 365}$

for each applicable $i$.

We also want our model to have two more properties:

• The first is that the temperature on each day in our model should be relatively close to the actual temperature of that day.
• The second is that our model needs to be smooth, so the change in temperature from day to day should be relatively small. The following objective would capture both properties:
$\sum_{i = 1}^n (s_i - x_i)^2 + \lambda \sum_{i = 2}^n(s_i - s_{i - 1})^2$

where $\lambda$ is the smoothing parameter. The larger $\lambda$ is, the smoother our model will be.

The following code uses Convex to find and plot the model:

yearly = Variable(n)
eq_constraints = [ yearly[i] == yearly[i - 365] for i in 365 + 1 : n ]

smoothing = 100
smooth_objective = sumsquares(yearly[1 : n - 1] - yearly[2 : n])
problem = minimize(sumsquares(temps - yearly) + smoothing * smooth_objective, eq_constraints);
solve!(problem, ECOSSolver(maxit=200, verbose=0))
residuals = temps - evaluate(yearly)

# Plot smooth fit
plot(1:n, temps[1:n], label="data")
plot!(1:n, evaluate(yearly)[1:n], linewidth=2, label="smooth fit",  ylabel="Temperature (°C)", xticks=0:365:n, xlabel="Time (days)")

We can also plot the residual temperatures, $r$, defined as $r = x - s$.

# Plot residuals for a few days
plot(1:100, residuals[1:100], ylabel="Residuals", xlabel="Time (days)")
root_mean_square_error = sqrt(sum( x -> x^2, residuals) / length(residuals))
2.734350653602959

Our smooth model has a RMS error of $2.7$, a significant improvement from just guessing the mean, but we can do better.

We now make the hypothesis that the residual temperature on a given day is some linear combination of the previous $5$ days. Such a model is called autoregressive. We are essentially trying to fit the residuals as a function of other parts of the data itself. We want to find a vector of coefficients $a$ such that

$\mbox{r}(i) \approx \sum_{j = 1}^5 a_j \mbox{r}(i - j)$

This can be done by simply minimizing the following sum of squares objective

$\sum_{i = 6}^n \left(\mbox{r}(i) - \sum_{j = 1}^5 a_j \mbox{r}(i - j)\right)^2$

The following Convex code solves this problem and plots our autoregressive model against the actual residual temperatures:

# Generate the residuals matrix
ar_len = 5

residuals_mat = Matrix{Float64}(undef, length(residuals) - ar_len, ar_len)
for i = 1:ar_len
residuals_mat[:, i] = residuals[ar_len - i + 1 : n - i]
end

# Solve autoregressive problem
ar_coef = Variable(ar_len)
problem = minimize(sumsquares(residuals_mat * ar_coef - residuals[ar_len + 1 : end]))
solve!(problem, ECOSSolver(max_iters=200, verbose=0))

# plot autoregressive fit of daily fluctuations for a few days
ar_range = 1:145
day_range = ar_range .+ ar_len
plot(day_range, residuals[day_range], label="fluctuations from smooth fit", ylabel="Temperature difference (°C)")
plot!(day_range, residuals_mat[ar_range, :] * evaluate(ar_coef), label="autoregressive estimate", xlabel="Time (days)")

Now, we can add our autoregressive model for the residual temperatures to our smooth model to get an better fitting model for the daily temperatures in the city of Melbourne:

total_estimate = evaluate(yearly)
total_estimate[ar_len + 1 : end] += residuals_mat * evaluate(ar_coef)
3643×1 Array{Float64,2}:
15.719842897500172
15.415291656832881
15.431030138168554
16.21772853230767
18.500928161534638
17.079674960305685
15.360402981609953
14.270024127640907
16.45924928723917
18.51510318011595
⋮
13.80907452224873
14.07231880307119
11.868428241428534
13.872250835971734
14.407355158243774
13.95986152861901
13.814515682337627
13.93332803510043
15.20661570341049 

We can plot the final fit of data across the whole time range:

plot(1:n, temps, label="data", ylabel="Temperature (°C)")
plot!(1:n, total_estimate, label="estimate", xticks=0:365:n, xlabel="Time (days)")

The RMS error of this final model is $\sim 2.3$:

root_mean_square_error = sqrt(sum( x -> x^2, total_estimate - temps) / length(temps))
2.3535771278941926