MIT

Robustly estimate trend and periodicity in a timeseries.

`Seasonal`

can recover sharp trend and period estimates from noisy
timeseries data with only a few periods. It is intended for
estimating season, trend, and level when initializing structural
timeseries models like Holt-Winters [Hyndman], and its defaults are
biased towards the kinds of training data that arise in that setting. Input
samples are assumed evenly-spaced from a continuous real-valued signal with
additive noise but no anomalies.

The seasonal estimate will be a list of period-over-period averages at each seasonal offset. You may specify a period length, or have it estimated from the data. The latter is an interesting capability of this package. If you're analyzing a single timeseries, you likely know its seasonality in advance. If you're sifting through a thousand series, collected at differing sampling rates, automatic period detection is a real convenience.

Trend removal in this package is in service of isolating and estimating the periodic (non-trend) variation. A lowpass smoothing of the data is removed from the original series, preserving original seasonal variation. Detrending is accomplishd by a coarse fitted spline, mean or median filters, or a fitted line.

In an addition to a python API for estimating seasonal offsets for your timeseries models, the seasonal package also provides executables to process CSV files from the command line:

`seasonal`

-- trend and seasonal decomposition`seasonal.trend`

-- trend decomposition only`seasonal.periodogram`

-- periodogram for raw or detrended data

It is not the aim of this package to construct explanatory models for
complete data sets as these commands do in their demonstration data
sets. You would be *much* better served building a proper STL or
ARIMA model in R. But these executables are convenient for exploring
seasonal's behavior and for troubleshooting surprises during use.

```
> pip install seasonal
> seasonal --demo
```

*(note: you'll need to have matplotlib installed for this demo. It is
not a formal dependency, as it is only needed when commands are given
options that produce graphical output)*

To install in-place so you can run tests and play with the included data sets:

```
> git clone git@github.com:welch/seasonal.git
> cd seasonal
> pip install -e .
> py.test -sv
.... (test output) ...
# let's analyze the classic air passenger data
> seasonal data/misc/logAirPassengers.12.csv
period %TEV %EEV N cycles file
12 90.99 89.18 144 12 data/misc/logAirPassengers.12.csv
# specify --plot to get a visualization (or --csv to get csv output)
> seasonal --plot data/misc/logAirPassengers.12.csv
```

```
>>> import math
>>> import numpy as np
>>> from seasonal import fit_seasons, adjust_seasons
>>> import matplotlib.pyplot as plt
>>>
>>> # make a trended sine wave
>>> s = [10 * math.sin(i * 2 * math.pi / 25) + i * i /100.0 for i in range(100)]
>>>
>>> # detrend and deseasonalize
>>> seasons, trend = fit_seasons(s)
>>> adjusted = adjust_seasons(s, seasons=seasons)
>>> residual = adjusted - trend
>>>
>>> # visualize results
>>> plt.figure()
>>> plt.plot(s, label='data')
>>> plt.plot(trend, label='trend')
>>> plt.plot(residual, label='residual')
>>> plt.legend(loc='upper left')
```

```
>>>
>>> # how about with some noise?
>>> noisy = s + np.random.normal(0, 5, len(s))
>>> seasons, trend = fit_seasons(noisy)
>>> adjusted = adjust_seasons(noisy, seasons=seasons)
>>> residual = adjusted - trend
>>>
>>> plt.figure()
>>> plt.plot(noisy, label='noisy')
>>> plt.plot(noisy - residual, label='trend+season')
>>> plt.plot(residual, label='residual')
>>> plt.legend(loc='upper left')
```

See docstrings for `fit_seasons()`

, `adjust_seasons()`

, `fit_trend()`

,
`adjust_trend()`

for much more detail. And see
`seasonal/application.py:seasonal_cmd()`

for code that creates
various combinations of detrended/deseasonalized series.

Sample code that uses `seasonal`

to initialize seasonal and trend
state in a Holt-Winters model is in `examples/hw.py`

. The
`estimate_state()`

function is the important thing; the rest is just
enough of a Holt-Winters implementation to demonstrate it.

You can run this from the seasonal directory like this:

```
> python examples/hw.py --demo
||seasons|| = 52.891
estimated alpha=0.361, beta=0.043, gamma=0.882
RMSE = 12.1125827022
final ||seasons|| = 248.801
```

This demo uses the classic air passenger data. The seasonality is multiplicative, but our model is additive. The initial 20% of the data is used to estimate the seasonal offsets, and the Holt-Winters updates gradually increase the magnitude of the seasonality to track the multiplicative changes. Try this with different values for --split (10 is so brief that no seasonality can be estimated, while 1.0 fits the initial state to the entire data set, leading to large errors in the early timesteps)

Although intended for use from within a python forecasting program,
the package is provided with three executables: `seasonal`

,
`seasonal.trend`

, and `seasonal.periodogram`

. Each of these reads CSV
files and can generate graphical or CSV output.

```
> seasonal -h
Usage: seasonal [options] csv-files...
Summarize trend and seasonality in a timeseries to stderr. The TEV (trend
explained variance) is the in-sample variance removed by detrending. The EEV
(expected explained variance) is the cross-validated out-of-sample detrended
variance explained by seasonality.
Options:
--version show program's version number and exit
-h, --help show this help message and exit
--column=COLUMN csv column to use (name or 0-based index, default
rightmost)
--split=SPLIT split data at the split*100% or int(split) point and use
the intial segment
--trend=TREND trend function (line, mean, median, spline). Default is
spline.
--thresh=THRESH Periodogram periods must score above thresh*maxscore
(default 0.9)
--minev=MINEV reject seasonality if it does not explain this percentage
of variance. Default is 0.05
--period=PERIOD seasonally adjust using this period
--csv write adjusted timeseries to stdout as CSV
-p, --plot display trend and seasonality using matplotlib
```

```
> seasonal.trend -h
Usage: seasonal.trend [options] csv-files...
Summarize trend in a timeseries to stderr. The %EV (explained variance) is the
in-sample variance removed by detrending.
Options:
--version show program's version number and exit
-h, --help show this help message and exit
--column=COLUMN csv column to use (name or 0-based index, default rightmost
-p, --plot display trend results using matplotlib
--trend=TREND trending function (line, mean, median, spline). Default is
spline
--period=PERIOD with --trend, adjust trend while preserving variation at
this periodicity (default based on data length)
--split=SPLIT split data at the split*100% or int(split) point and use
the intial segment
--csv write adjusted timeseries to stdout as CSV
```

```
> seasonal.periodogram -h
Usage: seasonal.periodogram [options] csv-files...
Display a periodogram, optionally detrending first.
Options:
--version show program's version number and exit
-h, --help show this help message and exit
--column=COLUMN csv column to use (name or 0-based index, default
rightmost)
--thresh=THRESH Retain periods scoring above thresh*maxscore (default 0.9)
-p, --plot display using matplotlib
--trend=TREND trending function (line, mean, median, spline). If
specified, perform an initial detrending using this filter
--period=PERIOD with --trend, adjust trend while preserving variation at
this periodicity (default based on data length)
--split=SPLIT split data at the split*100% or int(split) point and use
the intial segment
```

When initializing a timeseries model, it is customary to first detrend the training data by fitting a linear trend. But linear trend fitting with periodic data can be hazardous -- the trend you get depends on where you start and stop in the periodic cycle (as a simple demonstration, try linear regression on a few cycles of a sine wave). In this package, if a linear trend is selected, we use a broad median filter [Tukey] to knock down variations at the maximum expected period or less, and the Theil-Sen estimator [Sen] to robustly fit a slope to the result.

More usefully, trend forms other than straight lines can be specified. "Trend" is in the sense of Cleveland's STL decomposition [Cleveland] -- a lowpass smoothing of the data that, when removed from the original series, preserves original seasonal variation. Such detrending is accomplishd by a coarse fitted spline, mean or median filters, or a fitted line.

The default trend model uses piecewise cubic splines. The optional
median filter is a useful alternative, as it preserves edges and
prevents impulses from distorting the baseline signal level. Try
`seasonal.trend`

on data/NAB/art_daily* with `--trend median`

for a
demonstration of this. It would be interesting to autoselect between
these two trend models based on signal characteristics. Send me your
data!

Seasonality is represented as an offset for each seasonal interval. This is the same representation as is used in Holt-Winters. The representation does not enforce any kind of continuity from season to season, which is either a strength (sharp transitions are possible) or a weakness (noise is less efficiently rejected). Seasonal offsets for a given periodicity are estimated as period-over-period averages using all the provided data.

Estimating the period itself is less straightforward. Although a variety of techniques exist that operate in the frequency domain, they lean heavily on sinusoidal decomposition [Quinn] and don't recover sharp estimates for the short, noisy sequences we often get in model training sets. This package instead uses a time-domain approach that accommodates any periodic signal shape.

The period estimator tests a range of plausible periodicities for best fit to the detrended data. It is formulated as a model selection problem using cross-validated residual errors [Hastie]. The strength of the seasonal effect is also considered, using the R^2 of the leave-one-out cross-validation. For the seasonal model used here, this is the expected fraction of variance (after detrending) explained by the best seasonal estimate.

The time-domain approach is expensive (O(n^2)). As an optional
optimization, we first estimate a range of likely periods using a
fast, robust periodogram averaging technique
[Welch (no relation to the author)]. This works well for all our
tests and examples, but its calibration is ad-hoc and there are surely
classes of signal or levels of noise that will fool it. The
`seasonal.periodogram`

command provides a good visualization of this
part of the computation.

- [Cleveland] Cleveland, et al, "STL: A Seasonal-Trend Decomposition Based on Loess", http://cs.wellesley.edu/~cs315/Papers/stl%20statistical%20model.pdf
- [Hastie] Hastie, Tibshirani, and Friedman,
*The Elements of Statistical Learning (2nd ed)*, eqn 7.52, Springer, 2009 - [Hyndman] Hyndman and Athanasopoulosh,
*Forecasting: Principles and Practice*, https://www.otexts.org/fpp - [Quinn] Quinn and Hannan,
*The Estimation and Tracking of Frequency*, Sec. 6.2.3, Cambridge University Press, 2001 - [Sen] P.K. Sen, "Estimates of the regression coefficient based on Kendall's tau", J. Am. Stat. Assoc., Vol. 63, pp. 1379-1389, 1968.
- [Tukey] Tukey, J. W.,
*Exploratory Data Analysis*, Pearson, 1977 - [Welch] Welch, P.D. (1967) "The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms", IEEE Transactions on Audio Electroacoustics, AU-15, 70–73.

package: numpy, scipy extras: pandas, matplotlib

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