{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Automated ARIMA forecasting with Python\n",
"\n",
"Feng Li\n",
"\n",
"School of Statistics and Mathematics\n",
"\n",
"Central University of Finance and Economics\n",
"\n",
"[feng.li@cufe.edu.cn](mailto:feng.li@cufe.edu.cn)\n",
"\n",
"[https://feng.li/python](https://feng.li/python)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# ARIMA: Autoregressive Integrated Moving Average\n",
"\n",
"This notebook provides an example of how to train an ARIMA model to generate point forecasts of product sales in retail. We will train an ARIMA based model on the Orange Juice dataset.\n",
"\n",
"An ARIMA, which stands for AutoRegressive Integrated Moving Average, model can be created using an `ARIMA(p,d,q)` model within `statsmodels` library. In this notebook, we will be using an alternative library `pmdarima`, which allows us to automatically search for optimal ARIMA parameters, within a specified range. More specifically, we will be using `auto_arima` function within `pmdarima` to automatically discover the optimal parameters for an ARIMA model. This function wraps `ARIMA` and `SARIMAX` models of `statsmodels` library, that correspond to non-seasonal and seasonal model space, respectively."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"- In an ARIMA model there are 3 parameters that are used to help model the major aspects of a times series: seasonality, trend, and noise. These parameters are:\n",
" - **p** is the parameter associated with the auto-regressive aspect of the model, which incorporates past values.\n",
" - **d** is the parameter associated with the integrated part of the model, which effects the amount of differencing to apply to a time series.\n",
" - **q** is the parameter associated with the moving average part of the model.,\n",
"\n",
"- If our data has a seasonal component, we use a seasonal ARIMA model or `ARIMA(p,d,q)(P,D,Q)m`. In that case, we have an additional set of parameters: `P`, `D`, and `Q` which describe the autoregressive, differencing, and moving average terms for the seasonal part of the ARIMA model, and `m` refers to the number of periods in each season.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://mirrors.163.com/pypi/simple/\n",
"Requirement already satisfied: pmdarima in /home/fli/.local/lib/python3.9/site-packages (1.8.3)\n",
"Requirement already satisfied: pandas>=0.19 in /home/fli/.local/lib/python3.9/site-packages (from pmdarima) (1.3.4)\n",
"Requirement already satisfied: scipy>=1.3.2 in /usr/lib/python3/dist-packages (from pmdarima) (1.7.1)\n",
"Requirement already satisfied: urllib3 in /usr/lib/python3/dist-packages (from pmdarima) (1.26.5)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/lib/python3/dist-packages (from pmdarima) (0.17.0)\n",
"Requirement already satisfied: numpy>=1.19.3 in /usr/lib/python3/dist-packages (from pmdarima) (1.19.5)\n",
"Requirement already satisfied: Cython!=0.29.18,>=0.29 in /usr/lib/python3/dist-packages (from pmdarima) (0.29.24)\n",
"Requirement already satisfied: statsmodels!=0.12.0,>=0.11 in /usr/lib/python3/dist-packages (from pmdarima) (0.12.2)\n",
"Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/lib/python3/dist-packages (from pmdarima) (58.2.0)\n",
"Requirement already satisfied: scikit-learn>=0.22 in /usr/lib/python3/dist-packages (from pmdarima) (0.23.2)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/lib/python3/dist-packages (from pandas>=0.19->pmdarima) (2.8.1)\n",
"Requirement already satisfied: pytz>=2017.3 in /usr/lib/python3/dist-packages (from pandas>=0.19->pmdarima) (2021.3)\n"
]
}
],
"source": [
"! pip3 install pmdarima --user"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Global Settings and Imports"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"System version: 3.9.7 (default, Sep 24 2021, 09:43:00) \n",
"[GCC 10.3.0]\n"
]
}
],
"source": [
"import os\n",
"import sys\n",
"import math\n",
"import warnings\n",
"import itertools\n",
"import numpy as np\n",
"import pandas as pd\n",
"# import scrapbook as sb\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from pmdarima.arima import auto_arima\n",
"\n",
"pd.options.display.float_format = \"{:,.2f}\".format\n",
"np.set_printoptions(precision=2)\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"print(\"System version: {}\".format(sys.version))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Parameters\n",
"\n",
"Next, we define global settings related to the model. We will use historical weekly sales data only, without any covariate features to train the ARIMA model. The model parameter ranges are provided in params. These are later used by the `auto_arima()` function to search the space for the optimal set of parameters. To increase the space of models to search over, increase the `max_p` and `max_q` parameters.\n",
"\n",
"> NOTE: Our data does not show a strong seasonal component (as demonstrated in data exploration example notebook), so we will not be searching over the seasonal ARIMA models. To search over the seasonal models, set `seasonal` to `True` and include `start_P`, `start_Q`, `max_P`, and `max_Q` parameters in the auto_arima() function."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": [
"parameters"
]
},
"outputs": [],
"source": [
"# Forecasting settings\n",
"N_SPLITS = 1\n",
"HORIZON = 2\n",
"GAP = 2\n",
"FIRST_WEEK = 40\n",
"LAST_WEEK = 138\n",
"\n",
"# Parameters of ARIMA model\n",
"params = {\n",
" \"seasonal\": False,\n",
" \"start_p\": 0,\n",
" \"start_q\": 0,\n",
" \"max_p\": 5,\n",
" \"max_q\": 5,\n",
" \"m\": 52,\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Data Preparation\n",
"\n",
"\n",
"- We store the training data and test data using dataframes. \n",
"\n",
" - `train_df` contains the historical sales up to week 135 (the time we make forecasts) \n",
" - `aux_df` containing price/promotion information up until week 138. Here we assume that future price and promotion information up to a certain number of weeks ahead is predetermined and known. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
" \n",
"- In our example, we will be using historical sales only, and will not be using the `aux_df` data. The test data is stored in `test_df` which contains the sales of each product in week 137 and 138. \n",
"\n",
"- Assuming the current week is week 135, our goal is to forecast the sales in week 137 and 138 using the training data. There is a one-week gap between the current week and the first target week of forecasting as we want to leave time for planning inventory in practice."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"train_df = pd.read_csv(\"data/OrangeJuice_train.csv\")\n",
"test_df = pd.read_csv(\"data/OrangeJuice_test.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Process training data\n",
"\n",
"Our time series data is not complete, since we have missing sales for some stores/products and weeks. We will fill in those missing values by propagating the last valid observation forward to next available value. We will define functions for data frame processing, then use these functions within a loop that loops over each forecasting rounds.\n",
"\n",
"Note that our time series are grouped by `store` and `brand`, while `week` represents a time step, and `logmove` represents the value to predict.\n",
"\n",
"Let's first process the training data. Note that the training data runs from `FIRST_WEEK` to `LAST_WEEK - HORIZON - GAP + 1` as defined in Parameters section above."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"lines_to_next_cell": 2,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
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" store brand week logmove\n",
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"\n",
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},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Select only required columns\n",
"train_df = train_df[[\"store\", \"brand\", \"week\", \"logmove\"]]\n",
"train_df"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Notice that the unit sales of the products are given in logarithmic scale. We will use this quantity for training the forecasting model, as it smooths out the time series, and results in better forecasting performance. We will convert the `logmove` to a unit scale for evaluation, for consistency across our examples."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"lines_to_next_cell": 2,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"def df_from_cartesian_product(dict_in):\n",
" \"\"\"Generate a Pandas dataframe from Cartesian product of lists.\"\"\"\n",
" from itertools import product\n",
"\n",
" cart = list(product(*dict_in.values()))\n",
" df = pd.DataFrame(cart, columns=dict_in.keys())\n",
" return df\n",
"\n",
"def complete_and_fill_df(df, stores, brands, weeks):\n",
" \"\"\"Completes missing rows in Orange Juice datasets and fills in the missing values.\n",
" \"\"\"\n",
" d = {\"store\": stores, \"brand\": brands, \"week\": weeks}\n",
" data_grid = df_from_cartesian_product(d)\n",
" # Complete all rows\n",
" df_filled = pd.merge(data_grid, df, how=\"left\", on=[\"store\", \"brand\", \"week\"])\n",
" # Fill in missing values\n",
" df_filled = df_filled.groupby([\"store\", \"brand\"]).apply(lambda x: x.fillna(method=\"ffill\").fillna(method=\"bfill\"))\n",
" return df_filled"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
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},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a dataframe to hold all necessary data\n",
"store_list = train_df[\"store\"].unique()\n",
"brand_list = train_df[\"brand\"].unique()\n",
"train_week_list = range(FIRST_WEEK, LAST_WEEK - (HORIZON - 1) - (GAP - 1))\n",
"\n",
"train_filled = complete_and_fill_df(train_df, stores=store_list, brands=brand_list, weeks=train_week_list)\n",
"train_filled"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Process test data\n",
"\n",
"Let's now process the test data. Note that the test data runs from `LAST_WEEK - HORIZON + 1` to `LAST_WEEK`. Note that, in addition to filling out missing values, we also convert unit sales from logarithmic scale to the counts. We will do model training on the log scale, due to improved performance, however, we will transfrom the test data back into the unit scale (counts) by applying `math.exp()`, so that we can evaluate the performance on the unit scale."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
" store brand week logmove\n",
"566 2 6 126 8.52\n",
"567 2 6 127 8.03\n",
"568 2 6 128 8.15\n",
"569 2 6 129 8.03\n",
"570 2 6 130 7.74\n",
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"575 2 6 135 6.96"
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},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"STORE = 2\n",
"BRAND = 6\n",
"\n",
"train_ts = train_filled.loc[(train_filled.store == STORE) & (train_filled.brand == BRAND)]\n",
"train_ts.tail(10)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"ARIMA(order=(1, 0, 0), scoring_args={}, suppress_warnings=True)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_ts = np.array(train_ts.logmove)\n",
"\n",
"model = auto_arima(\n",
" train_ts,\n",
" seasonal=params[\"seasonal\"],\n",
" start_p=params[\"start_p\"],\n",
" start_q=params[\"start_q\"],\n",
" max_p=params[\"max_p\"],\n",
" max_q=params[\"max_q\"],\n",
" stepwise=True,\n",
")\n",
"\n",
"model.fit(train_ts)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Let's look at the model summary. As seen from the summary, the selected ARIMA model is `(p=1, d=0, q=0)`. This is a relatively simple model, also referred to as first-order auto-regressive model. It indicates that the time series is stationary and can be predicted as a multiple of its own previous value, plus a constant. This is an `ARIMA(1,0,0)+constant` model."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"
SARIMAX Results
\n",
"
\n",
"
Dep. Variable:
y
No. Observations:
96
\n",
"
\n",
"
\n",
"
Model:
SARIMAX(1, 0, 0)
Log Likelihood
-18.335
\n",
"
\n",
"
\n",
"
Date:
Fri, 12 Nov 2021
AIC
42.670
\n",
"
\n",
"
\n",
"
Time:
12:27:01
BIC
50.363
\n",
"
\n",
"
\n",
"
Sample:
0
HQIC
45.779
\n",
"
\n",
"
\n",
"
- 96
\n",
"
\n",
"
\n",
"
Covariance Type:
opg
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
coef
std err
z
P>|z|
[0.025
0.975]
\n",
"
\n",
"
\n",
"
intercept
3.5490
0.686
5.172
0.000
2.204
4.894
\n",
"
\n",
"
\n",
"
ar.L1
0.5579
0.086
6.468
0.000
0.389
0.727
\n",
"
\n",
"
\n",
"
sigma2
0.0855
0.012
6.973
0.000
0.061
0.109
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
Ljung-Box (L1) (Q):
0.21
Jarque-Bera (JB):
1.39
\n",
"
\n",
"
\n",
"
Prob(Q):
0.64
Prob(JB):
0.50
\n",
"
\n",
"
\n",
"
Heteroskedasticity (H):
1.48
Skew:
-0.28
\n",
"
\n",
"
\n",
"
Prob(H) (two-sided):
0.27
Kurtosis:
3.15
\n",
"
\n",
"
Warnings: [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
],
"text/plain": [
"\n",
"\"\"\"\n",
" SARIMAX Results \n",
"==============================================================================\n",
"Dep. Variable: y No. Observations: 96\n",
"Model: SARIMAX(1, 0, 0) Log Likelihood -18.335\n",
"Date: Fri, 12 Nov 2021 AIC 42.670\n",
"Time: 12:27:01 BIC 50.363\n",
"Sample: 0 HQIC 45.779\n",
" - 96 \n",
"Covariance Type: opg \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"intercept 3.5490 0.686 5.172 0.000 2.204 4.894\n",
"ar.L1 0.5579 0.086 6.468 0.000 0.389 0.727\n",
"sigma2 0.0855 0.012 6.973 0.000 0.061 0.109\n",
"===================================================================================\n",
"Ljung-Box (L1) (Q): 0.21 Jarque-Bera (JB): 1.39\n",
"Prob(Q): 0.64 Prob(JB): 0.50\n",
"Heteroskedasticity (H): 1.48 Skew: -0.28\n",
"Prob(H) (two-sided): 0.27 Kurtosis: 3.15\n",
"===================================================================================\n",
"\n",
"Warnings:\n",
"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
"\"\"\""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"The model summary contains a lot of information. The coefficient table in the middle provides the estimates for the weights of the respective p and q terms. Notice that the coefficient of the AR1 term has a low p-value (`P>|z|` column), indicating that this term is significant. It also shows that the constant term is significant with a low p-value.\n",
"\n",
"Next, let's also examine the diagnostics plot for the selected model."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
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"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"model.plot_diagnostics(figsize=(10, 8))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"In the top left, the residual errors fluctuate around a mean of zero and have a uniform variance, which may indicate that there is no bias in prediction. The density plot in the top right suggests normal distribution with mean zero.\n",
"\n",
"The Correlogram, or the ACF plot, in the lower right shows the residual errors are not autocorrelated. Any detected autocorrelation in this plot suggests that there may be some pattern in the residual errors which are not explained in the model, so adding additional predictors to the model may be beneficial.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"In the bottom left, we do not see significant deviation of residuals from the red line, which indicates that the model is a good fit.\n",
"\n",
"Overall, based on the above, it seems to that the model is a good fit for this data.\n",
"\n",
"\n",
"It is worth noting that selecting the best parameters for an ARIMA model can be challenging - somewhat subjective and time intesive, and should be done following a thorough data examination (seasonality, trend, bias). We use an `auto_arima()` function to search a provided space of parameters for the best model, mostly to demonstrate its usage and functionality.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Model evaluation\n",
"\n",
"Let's now take a look at the predictions. Since auto_arima model makes consecutive forecasts from the last time point, we want to forecast the next `n_periods = GAP + HORIZON - 1` points, so that we can account for the GAP, as described in the data setup. As mentioned above, we are also transforming our predictions from logarithmic scale to counts, for calculating evaluation metric."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/html": [
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