Sales Forecasting in Python using ARMA and Pandas on Sample Super Store Data Set¶

In this blog we will forecast sales in SampleSuper Store Data set. The forecasting algorithm we will use is Moving Average. For this we will use statsmodel library of python.
For Pre Data Processing we will use Pandas Library of Python

So, the first is to read Sample – Super store Data from a pre stored Excel File.

We will use monthly average sale as our aggregating standard

from statsmodels.tsa.arima_model import ARMA
import pandas as pd
import matplotlib.pyplot as plt
data=pd.read_excel('C:\\My\\Video\\sales_forecast\\Sample - Superstore.xls',header=0)
data

WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero

Now in this data set we have data for the year 2015, 2016, 2017 & 2018.
So, we will use the data from 2015 to 2017 as our training Data Set. And we will preidct 2018 Sales and compare it with Actual Sales value.

In this data set there are many products with different product categories. So, considering that each product category has different consumer behaviour and different seasonality effect we will select products for a particular category only.

So, to built a forecasting model we would only require below three columns :

(1) Order Date — > In order to compute the month and Year of transaction

(2) Cateogory — > To select transactions for only particular cateogory

(3) Sales — > Actual Value of sales

sales_data=data[['Order Date','Category','Sales']]
sales_data

Now using Data Frame we will select data where the value of Category is “Technology”

tech_sales_data = sales_data[(sales_data['Category'] == "Technology")]
tech_sales_data

Now, we will create three new column in Pandas Data Frame :

(1) Date – Contains Orde Date in datetime format form which Month & Year can be fetched

(2) year – Fetched from Date Column created above

(3) month – Fetched from Date column created above

tech_sales_data['date']=pd.to_datetime(tech_sales_data['Order Date'])
tech_sales_data['year'],tech_sales_data['month'] = tech_sales_data['date'].dt.year , tech_sales_data['date'].dt.month
tech_sales_data

C:\Sony\Downloads\WPy-3670\python-3.6.7.amd64\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """Entry point for launching an IPython kernel.
C:\Sony\Downloads\WPy-3670\python-3.6.7.amd64\lib\site-packages\ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

We will built a model to forecast monthly average sales of a product.

So, in data preparation we will group data based on year and month and aggregate it using mean() function on Pandas Data Frame

After aggregating we will select the data exclusing the year 2018 to get the training Data set

mean_sales=tech_sales_data.groupby(['year', 'month'],as_index=False).mean()
means_sales_train = mean_sales[(mean_sales['year']!=2018)]
means_sales_train

In order to feed the traniang Data set in to ARMA function imported from statsmodel library we need to convert it in to python list

There are two parameters passed to ARMA function. First is tranining Data set and second is order. Here order is taken as (0,10) based on our training Data.

Model is then fitted or trained.

yhat is a varaible on which predictions or forecasts are stored. In the preict fucniton we will pass on two indices based on which the number of future forecasts are made. Like in below code the difference between two indices plus one will give the number of forecats. In below code 12 future forecats will be made

sales = means_sales_train['Sales'].tolist()
model = ARMA(sales, order=(0, 10))
model_fit = model.fit(disp=False)
# make prediction
yhat = model_fit.predict(len(sales), len(sales)+11)
print(yhat)

[550.56875682 333.18444765 491.0191603  736.02698125 255.98230972
 323.15082923 399.21656013 467.50835595 478.7777673  405.95991681
 463.35587301 463.35587301]

C:\Sony\Downloads\WPy-3670\python-3.6.7.amd64\lib\site-packages\statsmodels\base\model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
  'available', HessianInversionWarning)

Now we have yhat where the forecated sales is stored.

In the varibale actual we will store the sales for the year 2018 which we can compare with forecasted sales value.

Since we have 12 predicted values so the varibale x_axis will have value for x_axis where we will plot actual and forecasted sales.

We will use matplotlib library to plot actual vs forecasted sales.

actual = mean_sales[(mean_sales['year']==2018)]['Sales'].tolist()
x_axis = [1,2,3,4,5,6,7,8,9,10,11,12]
plt.plot(x_axis,actual,color='blue')
plt.plot(x_axis,yhat,color='green')

[<matplotlib.lines.Line2D at 0x1b1b8c94358>]

	Row ID	Order ID	Order Date	Ship Date	Ship Mode	Customer ID	Customer Name	Segment	Country	City	…	Postal Code	Region	Product ID	Category	Sub-Category	Product Name	Sales	Quantity	Discount	Profit
0	1	CA-2017-152156	2017-11-08	2017-11-11	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	…	42420.0	South	FUR-BO-10001798	Furniture	Bookcases	Bush Somerset Collection Bookcase	261.9600	2	0.00	41.9136
1	2	CA-2017-152156	2017-11-08	2017-11-11	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	…	42420.0	South	FUR-CH-10000454	Furniture	Chairs	Hon Deluxe Fabric Upholstered Stacking Chairs,…	731.9400	3	0.00	219.5820
2	3	CA-2017-138688	2017-06-12	2017-06-16	Second Class	DV-13045	Darrin Van Huff	Corporate	United States	Los Angeles	…	90036.0	West	OFF-LA-10000240	Office Supplies	Labels	Self-Adhesive Address Labels for Typewriters b…	14.6200	2	0.00	6.8714
3	4	US-2016-108966	2016-10-11	2016-10-18	Standard Class	SO-20335	Sean O’Donnell	Consumer	United States	Fort Lauderdale	…	33311.0	South	FUR-TA-10000577	Furniture	Tables	Bretford CR4500 Series Slim Rectangular Table	957.5775	5	0.45	-383.0310
4	5	US-2016-108966	2016-10-11	2016-10-18	Standard Class	SO-20335	Sean O’Donnell	Consumer	United States	Fort Lauderdale	…	33311.0	South	OFF-ST-10000760	Office Supplies	Storage	Eldon Fold ‘N Roll Cart System	22.3680	2	0.20	2.5164
5	6	CA-2015-115812	2015-06-09	2015-06-14	Standard Class	BH-11710	Brosina Hoffman	Consumer	United States	Los Angeles	…	90032.0	West	FUR-FU-10001487	Furniture	Furnishings	Eldon Expressions Wood and Plastic Desk Access…	48.8600	7	0.00	14.1694
6	7	CA-2015-115812	2015-06-09	2015-06-14	Standard Class	BH-11710	Brosina Hoffman	Consumer	United States	Los Angeles	…	90032.0	West	OFF-AR-10002833	Office Supplies	Art	Newell 322	7.2800	4	0.00	1.9656
7	8	CA-2015-115812	2015-06-09	2015-06-14	Standard Class	BH-11710	Brosina Hoffman	Consumer	United States	Los Angeles	…	90032.0	West	TEC-PH-10002275	Technology	Phones	Mitel 5320 IP Phone VoIP phone	907.1520	6	0.20	90.7152
8	9	CA-2015-115812	2015-06-09	2015-06-14	Standard Class	BH-11710	Brosina Hoffman	Consumer	United States	Los Angeles	…	90032.0	West	OFF-BI-10003910	Office Supplies	Binders	DXL Angle-View Binders with Locking Rings by S…	18.5040	3	0.20	5.7825
9	10	CA-2015-115812	2015-06-09	2015-06-14	Standard Class	BH-11710	Brosina Hoffman	Consumer	United States	Los Angeles	…	90032.0	West	OFF-AP-10002892	Office Supplies	Appliances	Belkin F5C206VTEL 6 Outlet Surge	114.9000	5	0.00	34.4700
10	11	CA-2015-115812	2015-06-09	2015-06-14	Standard Class	BH-11710	Brosina Hoffman	Consumer	United States	Los Angeles	…	90032.0	West	FUR-TA-10001539	Furniture	Tables	Chromcraft Rectangular Conference Tables	1706.1840	9	0.20	85.3092
11	12	CA-2015-115812	2015-06-09	2015-06-14	Standard Class	BH-11710	Brosina Hoffman	Consumer	United States	Los Angeles	…	90032.0	West	TEC-PH-10002033	Technology	Phones	Konftel 250 Conference phone – Charcoal black	911.4240	4	0.20	68.3568
12	13	CA-2018-114412	2018-04-15	2018-04-20	Standard Class	AA-10480	Andrew Allen	Consumer	United States	Concord	…	28027.0	South	OFF-PA-10002365	Office Supplies	Paper	Xerox 1967	15.5520	3	0.20	5.4432
13	14	CA-2017-161389	2017-12-05	2017-12-10	Standard Class	IM-15070	Irene Maddox	Consumer	United States	Seattle	…	98103.0	West	OFF-BI-10003656	Office Supplies	Binders	Fellowes PB200 Plastic Comb Binding Machine	407.9760	3	0.20	132.5922
14	15	US-2016-118983	2016-11-22	2016-11-26	Standard Class	HP-14815	Harold Pawlan	Home Office	United States	Fort Worth	…	76106.0	Central	OFF-AP-10002311	Office Supplies	Appliances	Holmes Replacement Filter for HEPA Air Cleaner…	68.8100	5	0.80	-123.8580
15	16	US-2016-118983	2016-11-22	2016-11-26	Standard Class	HP-14815	Harold Pawlan	Home Office	United States	Fort Worth	…	76106.0	Central	OFF-BI-10000756	Office Supplies	Binders	Storex DuraTech Recycled Plastic Frosted Binders	2.5440	3	0.80	-3.8160
16	17	CA-2015-105893	2015-11-11	2015-11-18	Standard Class	PK-19075	Pete Kriz	Consumer	United States	Madison	…	53711.0	Central	OFF-ST-10004186	Office Supplies	Storage	Stur-D-Stor Shelving, Vertical 5-Shelf: 72″H x…	665.8800	6	0.00	13.3176
17	18	CA-2015-167164	2015-05-13	2015-05-15	Second Class	AG-10270	Alejandro Grove	Consumer	United States	West Jordan	…	84084.0	West	OFF-ST-10000107	Office Supplies	Storage	Fellowes Super Stor/Drawer	55.5000	2	0.00	9.9900
18	19	CA-2015-143336	2015-08-27	2015-09-01	Second Class	ZD-21925	Zuschuss Donatelli	Consumer	United States	San Francisco	…	94109.0	West	OFF-AR-10003056	Office Supplies	Art	Newell 341	8.5600	2	0.00	2.4824
19	20	CA-2015-143336	2015-08-27	2015-09-01	Second Class	ZD-21925	Zuschuss Donatelli	Consumer	United States	San Francisco	…	94109.0	West	TEC-PH-10001949	Technology	Phones	Cisco SPA 501G IP Phone	213.4800	3	0.20	16.0110
20	21	CA-2015-143336	2015-08-27	2015-09-01	Second Class	ZD-21925	Zuschuss Donatelli	Consumer	United States	San Francisco	…	94109.0	West	OFF-BI-10002215	Office Supplies	Binders	Wilson Jones Hanging View Binder, White, 1″	22.7200	4	0.20	7.3840
21	22	CA-2017-137330	2017-12-09	2017-12-13	Standard Class	KB-16585	Ken Black	Corporate	United States	Fremont	…	68025.0	Central	OFF-AR-10000246	Office Supplies	Art	Newell 318	19.4600	7	0.00	5.0596
22	23	CA-2017-137330	2017-12-09	2017-12-13	Standard Class	KB-16585	Ken Black	Corporate	United States	Fremont	…	68025.0	Central	OFF-AP-10001492	Office Supplies	Appliances	Acco Six-Outlet Power Strip, 4′ Cord Length	60.3400	7	0.00	15.6884
23	24	US-2018-156909	2018-07-16	2018-07-18	Second Class	SF-20065	Sandra Flanagan	Consumer	United States	Philadelphia	…	19140.0	East	FUR-CH-10002774	Furniture	Chairs	Global Deluxe Stacking Chair, Gray	71.3720	2	0.30	-1.0196
24	25	CA-2016-106320	2016-09-25	2016-09-30	Standard Class	EB-13870	Emily Burns	Consumer	United States	Orem	…	84057.0	West	FUR-TA-10000577	Furniture	Tables	Bretford CR4500 Series Slim Rectangular Table	1044.6300	3	0.00	240.2649
25	26	CA-2017-121755	2017-01-16	2017-01-20	Second Class	EH-13945	Eric Hoffmann	Consumer	United States	Los Angeles	…	90049.0	West	OFF-BI-10001634	Office Supplies	Binders	Wilson Jones Active Use Binders	11.6480	2	0.20	4.2224
26	27	CA-2017-121755	2017-01-16	2017-01-20	Second Class	EH-13945	Eric Hoffmann	Consumer	United States	Los Angeles	…	90049.0	West	TEC-AC-10003027	Technology	Accessories	Imation 8GB Mini TravelDrive USB 2.0 Flash Drive	90.5700	3	0.00	11.7741
27	28	US-2016-150630	2016-09-17	2016-09-21	Standard Class	TB-21520	Tracy Blumstein	Consumer	United States	Philadelphia	…	19140.0	East	FUR-BO-10004834	Furniture	Bookcases	Riverside Palais Royal Lawyers Bookcase, Royal…	3083.4300	7	0.50	-1665.0522
28	29	US-2016-150630	2016-09-17	2016-09-21	Standard Class	TB-21520	Tracy Blumstein	Consumer	United States	Philadelphia	…	19140.0	East	OFF-BI-10000474	Office Supplies	Binders	Avery Recycled Flexi-View Covers for Binding S…	9.6180	2	0.70	-7.0532
29	30	US-2016-150630	2016-09-17	2016-09-21	Standard Class	TB-21520	Tracy Blumstein	Consumer	United States	Philadelphia	…	19140.0	East	FUR-FU-10004848	Furniture	Furnishings	Howard Miller 13-3/4″ Diameter Brushed Chrome …	124.2000	3	0.20	15.5250
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
9964	9965	CA-2017-146374	2017-12-05	2017-12-10	Second Class	HE-14800	Harold Engle	Corporate	United States	Newark	…	19711.0	East	FUR-FU-10002671	Furniture	Furnishings	Electrix 20W Halogen Replacement Bulb for Zoom…	13.4000	1	0.00	6.4320
9965	9966	CA-2017-146374	2017-12-05	2017-12-10	Second Class	HE-14800	Harold Engle	Corporate	United States	Newark	…	19711.0	East	OFF-PA-10000349	Office Supplies	Paper	Easy-staple paper	4.9800	1	0.00	2.3406
9966	9967	CA-2017-146374	2017-12-05	2017-12-10	Second Class	HE-14800	Harold Engle	Corporate	United States	Newark	…	19711.0	East	OFF-EN-10004483	Office Supplies	Envelopes	#10 White Business Envelopes,4 1/8 x 9 1/2	109.6900	7	0.00	51.5543
9967	9968	CA-2018-153871	2018-12-11	2018-12-17	Standard Class	RB-19435	Richard Bierner	Consumer	United States	Plainfield	…	7060.0	East	OFF-BI-10004209	Office Supplies	Binders	Fellowes Twister Kit, Gray/Clear, 3/pkg	40.2000	5	0.00	18.0900
9968	9969	CA-2018-153871	2018-12-11	2018-12-17	Standard Class	RB-19435	Richard Bierner	Consumer	United States	Plainfield	…	7060.0	East	OFF-BI-10004600	Office Supplies	Binders	Ibico Ibimaster 300 Manual Binding System	735.9800	2	0.00	331.1910
9969	9970	CA-2018-153871	2018-12-11	2018-12-17	Standard Class	RB-19435	Richard Bierner	Consumer	United States	Plainfield	…	7060.0	East	OFF-AP-10003622	Office Supplies	Appliances	Bravo II Megaboss 12-Amp Hard Body Upright, Re…	22.7500	7	0.00	6.5975
9970	9971	CA-2016-103772	2016-06-28	2016-07-02	Standard Class	MP-17470	Mark Packer	Home Office	United States	Smyrna	…	30080.0	South	OFF-BI-10002867	Office Supplies	Binders	GBC Recycled Regency Composition Covers	119.5600	2	0.00	54.9976
9971	9972	CA-2016-103772	2016-06-28	2016-07-02	Standard Class	MP-17470	Mark Packer	Home Office	United States	Smyrna	…	30080.0	South	OFF-AR-10000538	Office Supplies	Art	Boston Model 1800 Electric Pencil Sharpener, Gray	140.7500	5	0.00	42.2250
9972	9973	CA-2017-130225	2017-09-11	2017-09-17	Standard Class	RC-19960	Ryan Crowe	Consumer	United States	Houston	…	77041.0	Central	OFF-EN-10000056	Office Supplies	Envelopes	Cameo Buff Policy Envelopes	99.5680	2	0.20	33.6042
9973	9974	US-2017-103674	2017-12-06	2017-12-10	Standard Class	AP-10720	Anne Pryor	Home Office	United States	Los Angeles	…	90032.0	West	TEC-PH-10004080	Technology	Phones	Avaya 5410 Digital phone	271.9600	5	0.20	27.1960
9974	9975	US-2017-103674	2017-12-06	2017-12-10	Standard Class	AP-10720	Anne Pryor	Home Office	United States	Los Angeles	…	90032.0	West	OFF-AR-10004752	Office Supplies	Art	Blackstonian Pencils	18.6900	7	0.00	5.2332
9975	9976	US-2017-103674	2017-12-06	2017-12-10	Standard Class	AP-10720	Anne Pryor	Home Office	United States	Los Angeles	…	90032.0	West	OFF-PA-10000743	Office Supplies	Paper	Xerox 1977	13.3600	2	0.00	6.4128
9976	9977	US-2017-103674	2017-12-06	2017-12-10	Standard Class	AP-10720	Anne Pryor	Home Office	United States	Los Angeles	…	90032.0	West	TEC-PH-10002496	Technology	Phones	Cisco SPA301	249.5840	2	0.20	31.1980
9977	9978	US-2017-103674	2017-12-06	2017-12-10	Standard Class	AP-10720	Anne Pryor	Home Office	United States	Los Angeles	…	90032.0	West	OFF-FA-10003467	Office Supplies	Fasteners	Alliance Big Bands Rubber Bands, 12/Pack	13.8600	7	0.00	0.0000
9978	9979	US-2017-103674	2017-12-06	2017-12-10	Standard Class	AP-10720	Anne Pryor	Home Office	United States	Los Angeles	…	90032.0	West	OFF-BI-10003727	Office Supplies	Binders	Avery Durable Slant Ring Binders With Label Ho…	13.3760	4	0.20	4.6816
9979	9980	US-2017-103674	2017-12-06	2017-12-10	Standard Class	AP-10720	Anne Pryor	Home Office	United States	Los Angeles	…	90032.0	West	OFF-BI-10002026	Office Supplies	Binders	Ibico Recycled Linen-Style Covers	437.4720	14	0.20	153.1152
9980	9981	US-2016-151435	2016-09-06	2016-09-09	Second Class	SW-20455	Shaun Weien	Consumer	United States	Lafayette	…	70506.0	South	FUR-TA-10001039	Furniture	Tables	KI Adjustable-Height Table	85.9800	1	0.00	22.3548
9981	9982	CA-2018-163566	2018-08-03	2018-08-06	First Class	TB-21055	Ted Butterfield	Consumer	United States	Fairfield	…	45014.0	East	OFF-LA-10004484	Office Supplies	Labels	Avery 476	16.5200	5	0.20	5.3690
9982	9983	US-2017-157728	2017-09-22	2017-09-28	Standard Class	RC-19960	Ryan Crowe	Consumer	United States	Grand Rapids	…	49505.0	Central	OFF-PA-10002195	Office Supplies	Paper	RSVP Cards & Envelopes, Blank White, 8-1/2″ X …	35.5600	7	0.00	16.7132
9983	9984	US-2017-157728	2017-09-22	2017-09-28	Standard Class	RC-19960	Ryan Crowe	Consumer	United States	Grand Rapids	…	49505.0	Central	TEC-PH-10001305	Technology	Phones	Panasonic KX TS208W Corded phone	97.9800	2	0.00	27.4344
9984	9985	CA-2016-100251	2016-05-17	2016-05-23	Standard Class	DV-13465	Dianna Vittorini	Consumer	United States	Long Beach	…	11561.0	East	OFF-LA-10003766	Office Supplies	Labels	Self-Adhesive Removable Labels	31.5000	10	0.00	15.1200
9985	9986	CA-2016-100251	2016-05-17	2016-05-23	Standard Class	DV-13465	Dianna Vittorini	Consumer	United States	Long Beach	…	11561.0	East	OFF-SU-10000898	Office Supplies	Supplies	Acme Hot Forged Carbon Steel Scissors with Nic…	55.6000	4	0.00	16.1240
9986	9987	CA-2017-125794	2017-09-29	2017-10-03	Standard Class	ML-17410	Maris LaWare	Consumer	United States	Los Angeles	…	90008.0	West	TEC-AC-10003399	Technology	Accessories	Memorex Mini Travel Drive 64 GB USB 2.0 Flash …	36.2400	1	0.00	15.2208
9987	9988	CA-2018-163629	2018-11-17	2018-11-21	Standard Class	RA-19885	Ruben Ausman	Corporate	United States	Athens	…	30605.0	South	TEC-AC-10001539	Technology	Accessories	Logitech G430 Surround Sound Gaming Headset wi…	79.9900	1	0.00	28.7964
9988	9989	CA-2018-163629	2018-11-17	2018-11-21	Standard Class	RA-19885	Ruben Ausman	Corporate	United States	Athens	…	30605.0	South	TEC-PH-10004006	Technology	Phones	Panasonic KX – TS880B Telephone	206.1000	5	0.00	55.6470
9989	9990	CA-2015-110422	2015-01-21	2015-01-23	Second Class	TB-21400	Tom Boeckenhauer	Consumer	United States	Miami	…	33180.0	South	FUR-FU-10001889	Furniture	Furnishings	Ultra Door Pull Handle	25.2480	3	0.20	4.1028
9990	9991	CA-2018-121258	2018-02-26	2018-03-03	Standard Class	DB-13060	Dave Brooks	Consumer	United States	Costa Mesa	…	92627.0	West	FUR-FU-10000747	Furniture	Furnishings	Tenex B1-RE Series Chair Mats for Low Pile Car…	91.9600	2	0.00	15.6332
9991	9992	CA-2018-121258	2018-02-26	2018-03-03	Standard Class	DB-13060	Dave Brooks	Consumer	United States	Costa Mesa	…	92627.0	West	TEC-PH-10003645	Technology	Phones	Aastra 57i VoIP phone	258.5760	2	0.20	19.3932
9992	9993	CA-2018-121258	2018-02-26	2018-03-03	Standard Class	DB-13060	Dave Brooks	Consumer	United States	Costa Mesa	…	92627.0	West	OFF-PA-10004041	Office Supplies	Paper	It’s Hot Message Books with Stickers, 2 3/4″ x 5″	29.6000	4	0.00	13.3200
9993	9994	CA-2018-119914	2018-05-04	2018-05-09	Second Class	CC-12220	Chris Cortes	Consumer	United States	Westminster	…	92683.0	West	OFF-AP-10002684	Office Supplies	Appliances	Acco 7-Outlet Masterpiece Power Center, Wihtou…	243.1600	2	0.00	72.9480

	Order Date	Category	Sales
0	2017-11-08	Furniture	261.9600
1	2017-11-08	Furniture	731.9400
2	2017-06-12	Office Supplies	14.6200
3	2016-10-11	Furniture	957.5775
4	2016-10-11	Office Supplies	22.3680
5	2015-06-09	Furniture	48.8600
6	2015-06-09	Office Supplies	7.2800
7	2015-06-09	Technology	907.1520
8	2015-06-09	Office Supplies	18.5040
9	2015-06-09	Office Supplies	114.9000
10	2015-06-09	Furniture	1706.1840
11	2015-06-09	Technology	911.4240
12	2018-04-15	Office Supplies	15.5520
13	2017-12-05	Office Supplies	407.9760
14	2016-11-22	Office Supplies	68.8100
15	2016-11-22	Office Supplies	2.5440
16	2015-11-11	Office Supplies	665.8800
17	2015-05-13	Office Supplies	55.5000
18	2015-08-27	Office Supplies	8.5600
19	2015-08-27	Technology	213.4800
20	2015-08-27	Office Supplies	22.7200
21	2017-12-09	Office Supplies	19.4600
22	2017-12-09	Office Supplies	60.3400
23	2018-07-16	Furniture	71.3720
24	2016-09-25	Furniture	1044.6300
25	2017-01-16	Office Supplies	11.6480
26	2017-01-16	Technology	90.5700
27	2016-09-17	Furniture	3083.4300
28	2016-09-17	Office Supplies	9.6180
29	2016-09-17	Furniture	124.2000
…	…	…	…
9964	2017-12-05	Furniture	13.4000
9965	2017-12-05	Office Supplies	4.9800
9966	2017-12-05	Office Supplies	109.6900
9967	2018-12-11	Office Supplies	40.2000
9968	2018-12-11	Office Supplies	735.9800
9969	2018-12-11	Office Supplies	22.7500
9970	2016-06-28	Office Supplies	119.5600
9971	2016-06-28	Office Supplies	140.7500
9972	2017-09-11	Office Supplies	99.5680
9973	2017-12-06	Technology	271.9600
9974	2017-12-06	Office Supplies	18.6900
9975	2017-12-06	Office Supplies	13.3600
9976	2017-12-06	Technology	249.5840
9977	2017-12-06	Office Supplies	13.8600
9978	2017-12-06	Office Supplies	13.3760
9979	2017-12-06	Office Supplies	437.4720
9980	2016-09-06	Furniture	85.9800
9981	2018-08-03	Office Supplies	16.5200
9982	2017-09-22	Office Supplies	35.5600
9983	2017-09-22	Technology	97.9800
9984	2016-05-17	Office Supplies	31.5000
9985	2016-05-17	Office Supplies	55.6000
9986	2017-09-29	Technology	36.2400
9987	2018-11-17	Technology	79.9900
9988	2018-11-17	Technology	206.1000
9989	2015-01-21	Furniture	25.2480
9990	2018-02-26	Furniture	91.9600
9991	2018-02-26	Technology	258.5760
9992	2018-02-26	Office Supplies	29.6000
9993	2018-05-04	Office Supplies	243.1600

	Order Date	Category	Sales
7	2015-06-09	Technology	907.152
11	2015-06-09	Technology	911.424
19	2015-08-27	Technology	213.480
26	2017-01-16	Technology	90.570
35	2017-12-08	Technology	1097.544
40	2016-12-27	Technology	371.168
41	2018-09-10	Technology	147.168
44	2017-03-11	Technology	45.980
47	2017-06-20	Technology	45.000
48	2017-06-20	Technology	21.800
54	2017-12-11	Technology	1029.950
59	2017-06-17	Technology	30.000
62	2016-11-24	Technology	13.980
68	2015-12-05	Technology	167.968
86	2018-10-26	Technology	19.990
90	2017-09-17	Technology	73.584
100	2017-08-29	Technology	95.976
103	2016-11-13	Technology	238.896
106	2018-11-23	Technology	74.112
107	2018-11-23	Technology	27.992
109	2016-10-15	Technology	339.960
123	2017-06-12	Technology	68.040
130	2018-02-02	Technology	59.970
147	2017-12-05	Technology	384.450
148	2017-12-05	Technology	149.970
152	2017-03-13	Technology	203.184
159	2017-11-20	Technology	944.930
161	2016-12-28	Technology	54.384
165	2015-09-08	Technology	8159.952
170	2015-09-08	Technology	143.976
…	…	…	…
9839	2017-09-25	Technology	479.976
9840	2017-09-25	Technology	44.736
9860	2018-01-14	Technology	67.800
9862	2018-01-14	Technology	12.120
9864	2018-01-14	Technology	159.800
9878	2018-04-20	Technology	122.382
9882	2015-08-12	Technology	272.610
9884	2015-04-03	Technology	62.310
9885	2015-04-03	Technology	159.980
9888	2016-08-08	Technology	79.990
9891	2017-07-25	Technology	124.250
9892	2017-07-25	Technology	297.550
9895	2015-09-26	Technology	153.824
9904	2015-11-12	Technology	127.984
9922	2018-05-06	Technology	191.968
9926	2016-03-20	Technology	199.980
9929	2017-09-04	Technology	2799.960
9933	2015-07-11	Technology	164.850
9941	2018-11-24	Technology	223.580
9946	2015-03-02	Technology	151.200
9949	2018-06-01	Technology	83.970
9950	2018-06-01	Technology	39.890
9960	2018-09-16	Technology	18.000
9973	2017-12-06	Technology	271.960
9976	2017-12-06	Technology	249.584
9983	2017-09-22	Technology	97.980
9986	2017-09-29	Technology	36.240
9987	2018-11-17	Technology	79.990
9988	2018-11-17	Technology	206.100
9991	2018-02-26	Technology	258.576

	Order Date	Category	Sales	date	year	month
7	2015-06-09	Technology	907.152	2015-06-09	2015	6
11	2015-06-09	Technology	911.424	2015-06-09	2015	6
19	2015-08-27	Technology	213.480	2015-08-27	2015	8
26	2017-01-16	Technology	90.570	2017-01-16	2017	1
35	2017-12-08	Technology	1097.544	2017-12-08	2017	12
40	2016-12-27	Technology	371.168	2016-12-27	2016	12
41	2018-09-10	Technology	147.168	2018-09-10	2018	9
44	2017-03-11	Technology	45.980	2017-03-11	2017	3
47	2017-06-20	Technology	45.000	2017-06-20	2017	6
48	2017-06-20	Technology	21.800	2017-06-20	2017	6
54	2017-12-11	Technology	1029.950	2017-12-11	2017	12
59	2017-06-17	Technology	30.000	2017-06-17	2017	6
62	2016-11-24	Technology	13.980	2016-11-24	2016	11
68	2015-12-05	Technology	167.968	2015-12-05	2015	12
86	2018-10-26	Technology	19.990	2018-10-26	2018	10
90	2017-09-17	Technology	73.584	2017-09-17	2017	9
100	2017-08-29	Technology	95.976	2017-08-29	2017	8
103	2016-11-13	Technology	238.896	2016-11-13	2016	11
106	2018-11-23	Technology	74.112	2018-11-23	2018	11
107	2018-11-23	Technology	27.992	2018-11-23	2018	11
109	2016-10-15	Technology	339.960	2016-10-15	2016	10
123	2017-06-12	Technology	68.040	2017-06-12	2017	6
130	2018-02-02	Technology	59.970	2018-02-02	2018	2
147	2017-12-05	Technology	384.450	2017-12-05	2017	12
148	2017-12-05	Technology	149.970	2017-12-05	2017	12
152	2017-03-13	Technology	203.184	2017-03-13	2017	3
159	2017-11-20	Technology	944.930	2017-11-20	2017	11
161	2016-12-28	Technology	54.384	2016-12-28	2016	12
165	2015-09-08	Technology	8159.952	2015-09-08	2015	9
170	2015-09-08	Technology	143.976	2015-09-08	2015	9
…	…	…	…	…	…	…
9839	2017-09-25	Technology	479.976	2017-09-25	2017	9
9840	2017-09-25	Technology	44.736	2017-09-25	2017	9
9860	2018-01-14	Technology	67.800	2018-01-14	2018	1
9862	2018-01-14	Technology	12.120	2018-01-14	2018	1
9864	2018-01-14	Technology	159.800	2018-01-14	2018	1
9878	2018-04-20	Technology	122.382	2018-04-20	2018	4
9882	2015-08-12	Technology	272.610	2015-08-12	2015	8
9884	2015-04-03	Technology	62.310	2015-04-03	2015	4
9885	2015-04-03	Technology	159.980	2015-04-03	2015	4
9888	2016-08-08	Technology	79.990	2016-08-08	2016	8
9891	2017-07-25	Technology	124.250	2017-07-25	2017	7
9892	2017-07-25	Technology	297.550	2017-07-25	2017	7
9895	2015-09-26	Technology	153.824	2015-09-26	2015	9
9904	2015-11-12	Technology	127.984	2015-11-12	2015	11
9922	2018-05-06	Technology	191.968	2018-05-06	2018	5
9926	2016-03-20	Technology	199.980	2016-03-20	2016	3
9929	2017-09-04	Technology	2799.960	2017-09-04	2017	9
9933	2015-07-11	Technology	164.850	2015-07-11	2015	7
9941	2018-11-24	Technology	223.580	2018-11-24	2018	11
9946	2015-03-02	Technology	151.200	2015-03-02	2015	3
9949	2018-06-01	Technology	83.970	2018-06-01	2018	6
9950	2018-06-01	Technology	39.890	2018-06-01	2018	6
9960	2018-09-16	Technology	18.000	2018-09-16	2018	9
9973	2017-12-06	Technology	271.960	2017-12-06	2017	12
9976	2017-12-06	Technology	249.584	2017-12-06	2017	12
9983	2017-09-22	Technology	97.980	2017-09-22	2017	9
9986	2017-09-29	Technology	36.240	2017-09-29	2017	9
9987	2018-11-17	Technology	79.990	2018-11-17	2018	11
9988	2018-11-17	Technology	206.100	2018-11-17	2018	11
9991	2018-02-26	Technology	258.576	2018-02-26	2018	2

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Forecasting on Sample Super Store Data set using Python – Pandas & ARMA

Sales Forecasting in Python using ARMA and Pandas on Sample Super Store Data Set¶

About Instrovate Technologies

	year	month	Sales
0	2015	1	349.254444
1	2015	2	201.063750
2	2015	3	1477.780636
3	2015	4	353.670538
4	2015	5	355.550963
5	2015	6	401.712619
6	2015	7	285.861929
7	2015	8	341.098519
8	2015	9	663.860261
9	2015	10	397.933933
10	2015	11	495.105049
11	2015	12	417.863840
12	2016	1	420.486727
13	2016	2	246.355000
14	2016	3	369.425786
15	2016	4	301.647351
16	2016	5	529.227273
17	2016	6	238.346889
18	2016	7	471.407545
19	2016	8	500.794581
20	2016	9	396.190167
21	2016	10	261.094878
22	2016	11	391.370508
23	2016	12	531.821313
24	2017	1	255.457545
25	2017	2	766.182125
26	2017	3	582.914919
27	2017	4	676.841000
28	2017	5	686.492643
29	2017	6	454.782000
30	2017	7	390.264706
31	2017	8	261.416270
32	2017	9	457.668120
33	2017	10	1087.357724
34	2017	11	371.795329
35	2017	12	365.946918