Modern Pandas (Part 7): Time Series

This is part 7 in my series on writing modern idiomatic pandas.

This post is available as a Jupyter notebook

Pandas started out in the financial world, so naturally it has strong time series support. The first half of this post will look at pandas' capabilities for manipulating time series data. The second half will discuss modeling time series data with statsmodels.

%matplotlib inline

import numpy as np
import pandas as pd
import as web
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style='ticks', context='talk')

Let's grab some stock data for Goldman Sachs using the pandas-datareader package, which spun off of pandas:

gs = web.DataReader("GS", data_source='yahoo', start='2006-01-01',
Open High Low Close Volume Adj Close
2006-01-03 126.699997 129.440002 124.230003 128.869995 6188700 114.660688
2006-01-04 127.349998 128.910004 126.379997 127.089996 4861600 113.076954
2006-01-05 126.000000 127.320000 125.610001 127.040001 3717400 113.032471
2006-01-06 127.290001 129.250000 127.290001 128.839996 4319600 114.633997
2006-01-09 128.500000 130.619995 128.000000 130.389999 4723500 116.013096

There isn't a special data-container just for time series in pandas, they're just Series or DataFrames with a DatetimeIndex. That said, DataFrames and Series with a DatetiemIndex do gain some special behaviors and additional methods.

Special Slicing

Looking at the elements of gs.index, we see that DatetimeIndexes are made up of pandas.Timestamps:

Timestamp('2006-01-03 00:00:00')

A Timestamp is mostly compatible with the builtin datetime.datetime class, but much amenable to storage in arrays.

Working with Timestamps can be awkward, so Series and DataFrames with DatetimeIndexes have some special slicing rules. The first special case is partial-string indexing. Say we wanted to select all the days in 2006. Even with Timestamp's convenient constructors, it's a pain.

Open High Low Close Volume Adj Close
2006-01-03 126.699997 129.440002 124.230003 128.869995 6188700 114.660688
2006-01-04 127.349998 128.910004 126.379997 127.089996 4861600 113.076954
2006-01-05 126.000000 127.320000 125.610001 127.040001 3717400 113.032471
2006-01-06 127.290001 129.250000 127.290001 128.839996 4319600 114.633997
2006-01-09 128.500000 130.619995 128.000000 130.389999 4723500 116.013096

Thanks to partial-string indexing, it's as simple as

Open High Low Close Volume Adj Close
2006-01-03 126.699997 129.440002 124.230003 128.869995 6188700 114.660688
2006-01-04 127.349998 128.910004 126.379997 127.089996 4861600 113.076954
2006-01-05 126.000000 127.320000 125.610001 127.040001 3717400 113.032471
2006-01-06 127.290001 129.250000 127.290001 128.839996 4319600 114.633997
2006-01-09 128.500000 130.619995 128.000000 130.389999 4723500 116.013096

Since label slicing is inclusive, this slice selects any observation where the year is 2006 (and partial-string indexing isn't limited to just years).

The second "convenience" is __getitem__ (square-bracket) fall-back indexing. I'm only going to mention it here, with the caveat that you should never use it. DataFrame __getitem__ typically looks in the column: gs['2006'] would search gs.columns for '2006', not find it, and raise a KeyError. But DataFrames with a DatetimeIndex catch that KeyError and try to slice the index. If it succeeds in slicing the index, the result like gs.loc['2006'] is returned. If it fails, the KeyError is re-raised. This is confusing because in pretty much every other case1 DataFrame.__getitem__ works on columns, and it's fragile because if you happened to have a column '2006' you would get just that column, and no fall-back indexing would occur. Just use gs.loc['2006'] when slicing DataFrame indexes.

Special Methods


Resampling is similar to a groupby: you split the time series into groups (5-day buckets below), apply a function to each group (mean), and combine the result (one row per group).

Open High Low Close Volume Adj Close
2006-01-03 126.834999 128.730002 125.877501 127.959997 4771825 113.851027
2006-01-08 130.349998 132.645000 130.205002 131.660000 4664300 117.143065
2006-01-13 131.510002 133.395005 131.244995 132.924995 3258250 118.268581
2006-01-18 132.210002 133.853333 131.656667 132.543335 4997766 118.001965
2006-01-23 133.771997 136.083997 133.310001 135.153998 3968500 120.476883
gs.resample("W").agg(['mean', 'sum']).head()
Open High Low Close Volume Adj Close
mean sum mean sum mean sum mean sum mean sum mean sum
2006-01-08 126.834999 507.339996 128.730002 514.920006 125.877501 503.510002 127.959997 511.839988 4771825 19087300 113.851027 455.404110
2006-01-15 130.684000 653.419998 132.848001 664.240006 130.544000 652.720001 131.979999 659.899994 4310420 21552100 117.427781 587.138903
2006-01-22 131.907501 527.630005 133.672501 534.690003 131.389999 525.559998 132.555000 530.220000 4653725 18614900 117.994103 471.976414
2006-01-29 133.771997 668.859986 136.083997 680.419983 133.310001 666.550003 135.153998 675.769989 3968500 19842500 120.476883 602.384416
2006-02-05 140.900000 704.500000 142.467999 712.339996 139.937998 699.689988 141.618002 708.090011 3920120 19600600 126.238926 631.194630

You can up-sample to convert to a higher frequency. The new points are filled with NaNs.

Open High Low Close Volume Adj Close
2006-01-03 00:00:00 126.699997 129.440002 124.230003 128.869995 6188700.0 114.660688
2006-01-03 06:00:00 NaN NaN NaN NaN NaN NaN
2006-01-03 12:00:00 NaN NaN NaN NaN NaN NaN
2006-01-03 18:00:00 NaN NaN NaN NaN NaN NaN
2006-01-04 00:00:00 127.349998 128.910004 126.379997 127.089996 4861600.0 113.076954

Rolling / Expanding / EW

These methods aren't unique to DatetimeIndexes, but they often make sense with time series, so I'll show them here.

gs.Close.rolling(28).mean().plot(label='28D MA')

plt.legend(bbox_to_anchor=(1.25, .5))


Each of .rolling, .expanding, and .ewm return a deferred object, similar to a GroupBy.

roll = gs.Close.rolling(30, center=True)
Rolling [window=30,center=True,axis=0]
m = roll.agg(['mean', 'std'])
ax = m['mean'].plot()
ax.fill_between(m.index, m['mean'] - m['std'], m['mean'] + m['std'],


Grab Bag


These are similar to dateutil.relativedelta, but they work with arrays.

gs.index + pd.DateOffset(months=3, days=-2)
DatetimeIndex(['2006-04-01', '2006-04-02', '2006-04-03', '2006-04-04',
               '2006-04-07', '2006-04-08', '2006-04-09', '2006-04-10',
               '2006-04-11', '2006-04-15',
               '2010-03-15', '2010-03-16', '2010-03-19', '2010-03-20',
               '2010-03-21', '2010-03-22', '2010-03-26', '2010-03-27',
               '2010-03-28', '2010-03-29'],
              dtype='datetime64[ns]', name='Date', length=1007, freq=None)

Holiday Calendars

There are a whole bunch of special calendars, useful for traders probably.

from import USColumbusDay

USColumbusDay.dates('2015-01-01', '2020-01-01')
DatetimeIndex(['2015-10-12', '2016-10-10', '2017-10-09', '2018-10-08',
              dtype='datetime64[ns]', freq='WOM-2MON')


Pandas works with pytz for nice timezone-aware datetimes. The typical workflow is

  1. localize timezone-naive timestamps to some timezone
  2. convert to desired timezone

If you already have timezone-aware Timestamps, there's no need for step one.

# tz naiive -> tz aware..... to desired UTC
Open High Low Close Volume Adj Close
2006-01-03 05:00:00+00:00 126.699997 129.440002 124.230003 128.869995 6188700 114.660688
2006-01-04 05:00:00+00:00 127.349998 128.910004 126.379997 127.089996 4861600 113.076954
2006-01-05 05:00:00+00:00 126.000000 127.320000 125.610001 127.040001 3717400 113.032471
2006-01-06 05:00:00+00:00 127.290001 129.250000 127.290001 128.839996 4319600 114.633997
2006-01-09 05:00:00+00:00 128.500000 130.619995 128.000000 130.389999 4723500 116.013096

Modeling Time Series

The rest of this post will focus on time series in the econometric sense. My indented reader for this section isn't all that clear, so I apologize upfront for any sudden shifts in complexity. I'm roughly targeting material that could be presented in a first or second semester applied statistics course, but with more hand-waving and less formality. I've put a whole bunch of resources at the end for people eager to learn more.

We'll focus on modelling Average Monthly Flights. If you've been following along in the series, you've seen most of this code for downloading the data before, so feel free to skip this next block.

import os
import io
import glob
import zipfile

import requests
import statsmodels.api as sm

def download_one(date):
    Download a single month's flights
    month = date.month
    year = date.year
    month_name = date.strftime('%B')
    headers = {
        'Pragma': 'no-cache',
        'Origin': '',
        'Accept-Encoding': 'gzip, deflate',
        'Accept-Language': 'en-US,en;q=0.8',
        'Upgrade-Insecure-Requests': '1',
        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.87 Safari/537.36',
        'Content-Type': 'application/x-www-form-urlencoded',
        'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
        'Cache-Control': 'no-cache',
        'Referer': '',
        'Connection': 'keep-alive',
        'DNT': '1',
    os.makedirs('timeseries', exist_ok=True)
    # long URL truncated, check the notebook
    data = 'UserTableName=On_Time_Performance&DBShortName=On_Time&RawDataTable=T_ONTIME&sqlstr=+SELECT+'

    r ='',
                      headers=headers, data=data.format(year=year, month=month, month_name=month_name),
    fp = os.path.join('timeseries', '{}-{}.zip'.format(year, month))

    with open(fp, 'wb') as f:
        for chunk in r.iter_content(chunk_size=1024):
            if chunk:
    return fp

def download_many(start, end):
    months = pd.date_range(start, end=end, freq='M')
    # We could easily parallelize this loop.
    for i, month in enumerate(months):

def unzip_one(fp):
    zf = zipfile.ZipFile(fp)
    csv = zf.extract(zf.filelist[0])
    return csv

def time_to_datetime(df, columns):
    Combine all time items into datetimes.

    2014-01-01,1149.0 -> 2014-01-01T11:49:00
    def converter(col):
        timepart = (col.astype(str)
                       .str.replace('\.0$', '')  # NaNs force float dtype
                       .str.pad(4, fillchar='0'))
        return  pd.to_datetime(df['fl_date'] + ' ' +
                               timepart.str.slice(0, 2) + ':' +
                               timepart.str.slice(2, 4),
        return datetime_part
    df[columns] = df[columns].apply(converter)
    return df

def read_one(fp):
    df = (pd.read_csv(fp, encoding='latin1')
            .drop('unnamed: 21', axis=1)
            .pipe(time_to_datetime, ['dep_time', 'arr_time', 'crs_arr_time',
            .assign(fl_date=lambda x: pd.to_datetime(x['fl_date'])))
    return df
download_many('2000-01-01', '2016-01-01')

zips = glob.glob(os.path.join('timeseries', '*.zip'))
csvs = [unzip_one(fp) for fp in zips]
dfs = [read_one(fp) for fp in csvs]
df = pd.concat(dfs, ignore_index=True)

cat_cols = ['unique_carrier', 'carrier', 'tail_num', 'origin', 'dest']

df[cat_cols] = df[cat_cols].apply(pd.Categorical)

df.to_hdf('ts.hdf5', 'ts', format='table')
df = pd.read_hdf('ts.hdf5', 'ts')

We can calculate the historical values with a resample.

daily = df.fl_date.value_counts().sort_index()
y = daily.resample('MS').mean()
2000-01-01    1882.387097
2000-02-01    1926.896552
2000-03-01    1951.000000
2000-04-01    1944.400000
2000-05-01    1957.967742
Freq: MS, Name: fl_date, dtype: float64

Note that I use the "MS" frequency code there. Pandas defaults to end of month (or end of year). Append an 'S' to get the start.

ax = y.plot()
ax.set(ylabel='Average Monthly Flights')


import statsmodels.formula.api as smf
import statsmodels.tsa.api as smt
import statsmodels.api as sm

One note of warning: I'm using the development version of statsmodels (commit de15ec8 to be precise). Not all of the items I've shown here are available in the currently-released version.

Think back to a typical regression problem, ignoring anything to do with time series for now. The usual task is to predict some value \(y\) using some a linear combination of features in \(X\).

$$y = \beta_0 + \beta_1 X_1 + \ldots + \beta_p X_p + \epsilon$$

When working with time series, some of the most important (and sometimes only) features are the previous, or lagged, values of \(y\).

Let's start by trying just that "manually": running a regression of y on lagged values of itself. We'll see that this regression suffers from a few problems: multicollinearity, autocorrelation, non-stationarity, and seasonality. I'll explain what each of those are in turn and why they're problems. Afterwards, we'll use a second model, seasonal ARIMA, which handles those problems for us.

First, let's create a dataframe with our lagged values of y using the .shift method, which shifts the index i periods, so it lines up with that observation.

X = (pd.concat([y.shift(i) for i in range(6)], axis=1,
               keys=['y'] + ['L%s' % i for i in range(1, 6)])
y L1 L2 L3 L4 L5
2000-06-01 1976.133333 1957.967742 1944.400000 1951.000000 1926.896552 1882.387097
2000-07-01 1937.032258 1976.133333 1957.967742 1944.400000 1951.000000 1926.896552
2000-08-01 1960.354839 1937.032258 1976.133333 1957.967742 1944.400000 1951.000000
2000-09-01 1900.533333 1960.354839 1937.032258 1976.133333 1957.967742 1944.400000
2000-10-01 1931.677419 1900.533333 1960.354839 1937.032258 1976.133333 1957.967742

We can fit the lagged model using statsmodels (which uses patsy to translate the formula string to a design matrix).

mod_lagged = smf.ols('y ~ trend + L1 + L2 + L3 + L4 + L5',
res_lagged =
OLS Regression Results
Dep. Variable: y R-squared: 0.881
Model: OLS Adj. R-squared: 0.877
Method: Least Squares F-statistic: 221.7
Date: Fri, 13 May 2016 Prob (F-statistic): 2.40e-80
Time: 16:14:16 Log-Likelihood: -1076.6
No. Observations: 187 AIC: 2167.
Df Residuals: 180 BIC: 2190.
Df Model: 6
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
Intercept 208.2440 65.495 3.180 0.002 79.008 337.480
trend -0.1123 0.106 -1.055 0.293 -0.322 0.098
L1 1.0489 0.075 14.052 0.000 0.902 1.196
L2 -0.0001 0.108 -0.001 0.999 -0.213 0.213
L3 -0.1450 0.108 -1.346 0.180 -0.358 0.068
L4 -0.0393 0.109 -0.361 0.719 -0.254 0.175
L5 0.0506 0.074 0.682 0.496 -0.096 0.197
Omnibus: 55.872 Durbin-Watson: 2.009
Prob(Omnibus): 0.000 Jarque-Bera (JB): 322.488
Skew: 0.956 Prob(JB): 9.39e-71
Kurtosis: 9.142 Cond. No. 5.97e+04

There are a few problems with this approach though. Since our lagged values are highly correlated with each other, our regression suffers from multicollinearity. That ruins our estimates of the slopes.



Second, we'd intuitively expect the \(\beta_i\)s to gradually decline to zero. The immediately preceding period should be most important (\(\beta_1\) is the largest coefficient in absolute value), followed by \(\beta_2\), and \(\beta_3\)... Looking at the regression summary and the bar graph below, this isn't the case (the cause is related to multicollinearity).

res_lagged.params.drop(['Intercept', 'trend'])


Finally, our degrees of freedom drop since we lose two for each variable (one for estimating the coefficient, one for the lost observation as a result of the shift). At least in (macro)econometrics, each observation is precious and we're loath to throw them away, though sometimes that's unavoidable.


Another problem our lagged model suffered from is autocorrelation (also know as serial correlation). Roughly speaking, autocorrelation is when there's a clear pattern in the residuals of your regression (the observed minus the predicted). Let's fit a simple model of \(y = \beta_0 + \beta_1 T + \epsilon\), where T is the time trend (np.arange(len(y))).

# `Results.resid` is a Series of residuals: y - ŷ
mod_trend = sm.OLS.from_formula(
    'y ~ trend', data=y.to_frame(name='y')
res_trend =

Residuals (the observed minus the expected, or \(\hat{e_t} = y_t - \hat{y_t}\)) are supposed to be white noise. That's one of the assumptions many of the properties of linear regression are founded upon. In this case there's a correlation between one residual and the next: if the residual at time \(t\) was above expectation, then the residual at time \(t + 1\) is much more likely to be above average as well (\(e_t > 0 \implies E_t[e_{t+1}] > 0\)).

We'll define a helper function to plot the residuals time series, and some diagnostics about them.

def tsplot(y, lags=None, figsize=(10, 8)):
    fig = plt.figure(figsize=figsize)
    layout = (2, 2)
    ts_ax = plt.subplot2grid(layout, (0, 0), colspan=2)
    acf_ax = plt.subplot2grid(layout, (1, 0))
    pacf_ax = plt.subplot2grid(layout, (1, 1))

    y.plot(ax=ts_ax), lags=lags, ax=acf_ax), lags=lags, ax=pacf_ax)
    [ax.set_xlim(1.5) for ax in [acf_ax, pacf_ax]]
    return ts_ax, acf_ax, pacf_ax

Calling it on the residuals from the linear trend:

tsplot(res_trend.resid, lags=36)


The top subplot shows the time series of our residuals \(e_t\), which should be white noise (but it isn't). The bottom shows the autocorrelation of the residuals as a correlogram. It measures the correlation between a value and it's lagged self, e.g. \(corr(e_t, e_{t-1}), corr(e_t, e_{t-2}), \ldots\). The partial autocorrelation plot in the bottom-right shows a similar concept. It's partial in the sense that the value for \(corr(e_t, e_{t-k})\) is the correlation between those two periods, after controlling for the values at all shorter lags.

Autocorrelation is a problem in regular regressions like above, but we'll use it to our advantage when we setup an ARIMA model below. The basic idea is pretty sensible: if your regression residuals have a clear pattern, then there's clearly some structure in the data that you aren't taking advantage of. If a positive residual today means you'll likely have a positive residual tomorrow, why not incorporate that information into your forecast, and lower your forecasted value for tomorrow? That's pretty much what ARIMA does.


It's important that your dataset be stationary, otherwise you run the risk of finding spurious correlations. A common example is the relationship between number of TVs per person and life expectancy. It's not likely that there's an actual causal relationship there. Rather, there could be a third variable that's driving both (wealth, say). Granger and Newbold (1974) had some stern words for the econometrics literature on this.

We find it very curious that whereas virtually every textbook on econometric methodology contains explicit warnings of the dangers of autocorrelated errors, this phenomenon crops up so frequently in well-respected applied work.

(:fire:), but in that academic passive-aggressive way.

The typical way to handle non-stationarity is to difference the non-stationary variable until is is stationary.

y.to_frame(name='y').assign(Δy=lambda x: x.y.diff()).plot(subplots=True)


Our original series actually doesn't look that bad. It doesn't look like nominal GDP say, where there's a clearly rising trend. But we have more rigorous methods for detecting whether a series is non-stationary than simply plotting and squinting at it. One popular method is the Augmented Dickey-Fuller test. It's a statistical hypothesis test that roughly says:

\(H_0\) (null hypothesis): \(y\) is non-stationary, needs to be differenced

\(H_A\) (alternative hypothesis): \(y\) is stationary, doesn't need to be differenced

I don't want to get into the weeds on exactly what the test statistic is, and what the distribution looks like. This is implemented in statsmodels as smt.adfuller. The return type is a bit busy for me, so we'll wrap it in a namedtuple.

from collections import namedtuple

ADF = namedtuple("ADF", "adf pvalue usedlag nobs critical icbest")
ADF(*smt.adfuller(y))._asdict()  # for pretty-printing
OrderedDict([('adf', -1.9904608794641487),
             ('pvalue', 0.29077127047555601),
             ('usedlag', 15),
             ('nobs', 176),
              {'1%': -3.4680615871598537,
               '10%': -2.5756015922004134,
               '5%': -2.8781061899535128}),
             ('icbest', 1987.6605732826176)])

So we failed to reject the null hypothesis that the original series was non-stationary. Let's difference it.

OrderedDict([('adf', -3.5862361055645211),
             ('pvalue', 0.0060296818910968268),
             ('usedlag', 14),
             ('nobs', 176),
              {'1%': -3.4680615871598537,
               '10%': -2.5756015922004134,
               '5%': -2.8781061899535128}),
             ('icbest', 1979.6445486427308)])

This looks better. We'll fit another OLS model of \(\Delta y = \beta_0 + \beta_1 L \Delta y_{t-1} + e_t\)

data = (y.to_frame(name='y')
         .assign(Δy=lambda df: df.y.diff())
         .assign(LΔy=lambda df: df.Δy.shift()))
mod_stationary = smf.ols('Δy ~ LΔy', data=data.dropna())
res_stationary =
tsplot(res_stationary.resid, lags=24)


This is better, but we still see a regular cycle in the residuals. The cause is seasonality.


We have strong monthly seasonality:



There are a few ways to handle seasonality. We'll just rely on the SARIMAX method to do it for us. For now, recognize that it's a problem to be solved.


So, we've sketched the problems with regular old regression: multicollinearity, autocorrelation, non-stationarity, and seasonality. Our tool of choice, smt.SARIMAX, which stands for Seasonal ARIMA with eXogenous regressors, can handle all these. We'll walk through the components in pieces.

ARIMA stands for AutoRegressive Integrated Moving Average. It's a relatively simple yet flexible way of modeling univariate time series. It's made up of three components, and is typically written as \(\mathrm{ARIMA}(p, d, q)\).


The idea is to predict a variable by a linear combination of its lagged values (auto-regressive as in regressing a value on its past self). An AR(p), where \(p\) represents the number of lagged values used, is written as

$$y_t = c + \phi_1 y_{t-1} + \phi_2 y_{t-2} + \ldots + \phi_p y_{t-p} + e_t$$

\(c\) is a constant and \(e_t\) is white noise. This looks a lot like a linear regression model with multiple predictors, but the predictors happen to be lagged values of \(y\) (though they are estimated differently).


Integrated is like the opposite of differencing, and is the part that deals with stationarity. If you have to difference your dataset 1 time to get it stationary, then \(d=1\), and your series is integrated of order 1. We'll introduce one bit of notation for differencing: \(\Delta y_t = y_t - y_{t-1}\) for \(d=1\).

Moving Average

MA models look somewhat similar to the AR component, but it's dealing with different values.

$$y_t = c + e_t + \theta_1 e_{t-1} + \theta_2 e_{t-2} + \ldots + \theta_q e_{t-q}$$

\(c\) again is a constant and \(e_t\) again is white noise. But now the coefficients are multiplying the residuals from previous predictions, not the actual (or differenced) values.


Putting that together, an ARIMA(1, 1, 1) process is written as

$$\Delta y_t = c + \phi_1 \Delta y_{t-1} + \theta_t e_{t-1} + e_t$$

Using lag notation, where \(L y_t = y_{t-1}\), i.e. y.shift() in pandas, we can rewrite that as

$$(1 - \phi_1 L) (1 - L)y_t = c + (1 + \theta L)e_t$$

That was for our specific \(\mathrm{ARIMA}(1, 1, 1)\) model. For the general \(\mathrm{ARIMA}(p, d, q)\), that becomes

$$(1 - \phi_1 L - \ldots - \phi_p L^p) (1 - L)^d y_t = c + (1 + \theta L + \ldots + \theta_q L^q)e_t$$

We went through that extremely quickly, so don't feel bad if things aren't clear. Fortunately, the model is pretty easy to use with statsmodels.

mod = smt.SARIMAX(y, trend='c', order=(1, 1, 1))
res =
tsplot(res.resid[2:], lags=24)


Statespace Model Results
Dep. Variable: fl_date No. Observations: 192
Model: SARIMAX(1, 1, 1) Log Likelihood -1104.663
Date: Fri, 13 May 2016 AIC 2217.326
Time: 16:16:27 BIC 2230.356
Sample: 01-01-2000 HQIC 2222.603
- 12-01-2015
Covariance Type: opg
coef std err z P>|z| [0.025 0.975]
intercept 0.7993 4.959 0.161 0.872 -8.921 10.519
ar.L1 0.3515 0.564 0.623 0.533 -0.754 1.457
ma.L1 -0.2310 0.577 -0.400 0.689 -1.361 0.899
sigma2 6181.2832 350.439 17.639 0.000 5494.435 6868.131
Ljung-Box (Q): 209.30 Jarque-Bera (JB): 424.36
Prob(Q): 0.00 Prob(JB): 0.00
Heteroskedasticity (H): 0.86 Skew: 1.15
Prob(H) (two-sided): 0.54 Kurtosis: 9.93

There's a bunch of output there with various tests, estimated parameters, and information criteria. Let's just say that things are looking better, but we still haven't accounted for seasonality.

A seasonal ARIMA model is written as \(\mathrm{ARIMA}(p,d,q)×(P,D,Q)_s\). Lowercase letters are for the non-seasonal component, just like before. Upper-case letters are a similar specification for the seasonal component, where \(s\) is the periodicity (4 for quarterly, 12 for monthly).

It's like we have two processes, one for non-seasonal component and one for seasonal components, and we multiply them together with regular algebra rules.

The general form of that looks like (quoting the statsmodels docs here)

$$\phi_p(L)\tilde{\phi}_P(L^S)\Delta^d\Delta_s^D y_t = A(t) + \theta_q(L)\tilde{\theta}_Q(L^s)e_t$$


I don't find that to be very clear, but maybe an example will help. We'll fit a seasonal ARIMA\((2,0,1)×(0, 1, 2)_{12}\).

So the nonseasonal component is

And the seasonal component is

mod_seasonal = smt.SARIMAX(y, trend='c',
                           order=(1, 1, 2), seasonal_order=(0, 1, 2, 12))
res_seasonal =
Statespace Model Results
Dep. Variable: fl_date No. Observations: 192
Model: SARIMAX(1, 1, 2)x(0, 1, 2, 12) Log Likelihood -992.148
Date: Fri, 13 May 2016 AIC 1998.297
Time: 16:16:31 BIC 2021.099
Sample: 01-01-2000 HQIC 2007.532
- 12-01-2015
Covariance Type: opg
coef std err z P>|z| [0.025 0.975]
intercept 0.7824 5.279 0.148 0.882 -9.564 11.129
ar.L1 -0.9880 0.374 -2.639 0.008 -1.722 -0.254
ma.L1 0.9905 0.437 2.265 0.024 0.133 1.847
ma.L2 0.0041 0.091 0.045 0.964 -0.174 0.182
ma.S.L12 -0.7869 0.066 -11.972 0.000 -0.916 -0.658
ma.S.L24 0.2121 0.063 3.366 0.001 0.089 0.336
sigma2 3645.3299 219.296 16.623 0.000 3215.518 4075.142
Ljung-Box (Q): 47.28 Jarque-Bera (JB): 464.42
Prob(Q): 0.20 Prob(JB): 0.00
Heteroskedasticity (H): 0.29 Skew: -1.30
Prob(H) (two-sided): 0.00 Kurtosis: 10.45
tsplot(res_seasonal.resid[12:], lags=24)


Things look much better now.

One thing I didn't really talk about is order selection. How to choose \(p, d, q, P, D\) and \(Q\). R's forecast package does have a handy auto.arima function that does this for you. Python / statsmodels don't have that at the minute. The alternative seems to be experience (boo), intuition (boo), and good-old grid-search. You can fit a bunch of models for a bunch of combinations of the parameters and use the AIC or BIC to choose the best. Here is a useful reference, and this StackOverflow answer recommends a few options.


Now that we fit that model, let's put it to use. First, we'll make a bunch of one-step ahead forecasts. At each point (month), we take the history up to that point and make a forecast for the next month. So the forecast for January 2014 has available all the data up through December 2013.

pred = res_seasonal.get_prediction(start='2001-03-01')
pred_ci = pred.conf_int()
ax = y.plot(label='observed')
pred.predicted_mean.plot(ax=ax, label='Forecast', alpha=.7)
                pred_ci.iloc[:, 0],
                pred_ci.iloc[:, 1], color='k', alpha=.2)


There are a few places where the observed series slips outside the 95% confidence interval. The series seems especially unstable before 2005.

Alternatively, we can make dynamic forecasts as of some month (January 2013 in the example below). That means the forecast from that point forward only use information available as of January 2013. The predictions are generated in a similar way: a bunch of one-step forecasts. Only instead of plugging in the actual values beyond January 2013, we plug in the forecast values.

pred_dy = res_seasonal.get_prediction(start='2002-03-01', dynamic='2013-01-01')
pred_dy_ci = pred_dy.conf_int()
ax = y.plot(label='observed')
pred_dy.predicted_mean.plot(ax=ax, label='Forecast')
                pred_dy_ci.iloc[:, 0],
                pred_dy_ci.iloc[:, 1], color='k', alpha=.25)

ax.fill_betweenx(ax.get_ylim(), pd.Timestamp('2013-01-01'), y.index[-1],
                 alpha=.1, zorder=-1)
ax.annotate('Dynamic $\\longrightarrow$', (pd.Timestamp('2013-02-01'), 550))




This is a collection of links for those interested.

Time series modeling in Python

General Textbooks


Congratulations if you made it this far, this piece just kept growing (and I still had to cut stuff). The main thing cut was talking about how SARIMAX is implemented on top of using statsmodels' statespace framework. The statespace framework, developed mostly by Chad Fulton over the past couple years, is really nice. You can pretty easily extend it with custom models, but still get all the benefits of the framework's estimation and results facilities. I'd recommend reading the notebooks. We also didn't get to talk at all about Skipper Seabold's work on VARs, but maybe some other time.

As always, feedback is welcome.

  1. The only other one is boolean indexing. I think that one is fine since you're passing in an array of booleans, not a label.