Question

Problem 1 for the Data Set Shoe-sales.csv:

You are an analyst in the IJK shoe company and you are expected to forecast the sales of the pairs of shoes for the upcoming 12 months from where the data ends. The data for the pair of shoe sales has been given to you from January,1980 to July,1995.

Problem 2 for the Data Set SoftDrink.csv:

You are an analyst in the RST soft drink company and you are expected to forecast the sales of the production of the soft drink for the upcoming 12 months from where the data ends. The data for the production of soft drink has been given to you from January,1980 to July,1995.

Please do perform the following questions on each of these two data sets separately.

RUBRIC

Questions Points

1. Read the data as an appropriate Time Series data and plot the data. 2

2. Perform appropriate Exploratory Data Analysis to understand the data and also perform

decomposition. 6

3. Split the data into training and test. The test data should start in 1991. 2

4. Build various exponential smoothing models on the training data and evaluate the model

using RMSE on the test data. Other models such as regression, naive forecast models, simple

average models etc. should also be built on the training data and check the performance on

the test data using RMSE. 18

5. Check for the stationarity of the data on which the model is being built on using

appropriate statistical tests and also mention the hypothesis for the statistical test. If the data

is found to be non-stationary, take appropriate steps to make it stationary. Check the new data

for stationarity and comment. Note: Stationarity should be checked at alpha = 0.05. 3

6. Build an automated version of the ARIMA/SARIMA model in which the parameters are

selected using the lowest Akaike Information Criteria (AIC) on the training data and evaluate

this model on the test data using RMSE. 10

7. Build ARIMA/SARIMA models based on the cut-off points of ACF and PACF on the

training data and evaluate this model on the test data using RMSE. 9

8. Build a table with all the models built along with their corresponding parameters and the

respective RMSE values on the test data. 2

9. Based on the model-building exercise, build the most optimum model(s) on the complete

data and predict 12 months into the future with appropriate confidence intervals/bands.

3

10. Comment on the model thus built and report your findings and suggest the measures

that the company should be taking for future sales.

need both code and explainations

SHoe sales dataset

YearMonth	Shoe_Sales
1980-01	85
1980-02	89
1980-03	109
1980-04	95
1980-05	91
1980-06	95
1980-07	96
1980-08	128
1980-09	124
1980-10	111
1980-11	178
1980-12	140
1981-01	150
1981-02	132
1981-03	155
1981-04	132
1981-05	91
1981-06	94
1981-07	109
1981-08	155
1981-09	123
1981-10	130
1981-11	150
1981-12	163
1982-01	101
1982-02	123
1982-03	127
1982-04	112
1982-05	108
1982-06	116
1982-07	153
1982-08	163
1982-09	128
1982-10	142
1982-11	170
1982-12	214
1983-01	134
1983-02	122
1983-03	142
1983-04	156
1983-05	145
1983-06	169
1983-07	134
1983-08	165
1983-09	156
1983-10	111
1983-11	165
1983-12	197
1984-01	124
1984-02	124
1984-03	139
1984-04	137
1984-05	127
1984-06	134
1984-07	136
1984-08	171
1984-09	112
1984-10	110
1984-11	147
1984-12	196
1985-01	112
1985-02	118
1985-03	125
1985-04	122
1985-05	120
1985-06	118
1985-07	281
1985-08	344
1985-09	366
1985-10	362
1985-11	580
1985-12	523
1986-01	348
1986-02	246
1986-03	197
1986-04	306
1986-05	279
1986-06	280
1986-07	358
1986-08	431
1986-09	448
1986-10	433
1986-11	504
1986-12	579
1987-01	384
1987-02	335
1987-03	320
1987-04	496
1987-05	448
1987-06	377
1987-07	523
1987-08	468
1987-09	428
1987-10	520
1987-11	493
1987-12	662
1988-01	304
1988-02	308
1988-03	313
1988-04	328
1988-05	354
1988-06	338
1988-07	483
1988-08	355
1988-09	439
1988-10	290
1988-11	352
1988-12	454
1989-01	306
1989-02	303
1989-03	344
1989-04	254
1989-05	309
1989-06	310
1989-07	379
1989-08	294
1989-09	356
1989-10	318
1989-11	405
1989-12	545
1990-01	268
1990-02	243
1990-03	273
1990-04	273
1990-05	236
1990-06	222
1990-07	302
1990-08	285
1990-09	309
1990-10	322
1990-11	362
1990-12	471
1991-01	198
1991-02	253
1991-03	173
1991-04	186
1991-05	185
1991-06	105
1991-07	228
1991-08	214
1991-09	189
1991-10	270
1991-11	277
1991-12	378
1992-01	185
1992-02	182
1992-03	258
1992-04	179
1992-05	197
1992-06	168
1992-07	250
1992-08	211
1992-09	260
1992-10	234
1992-11	305
1992-12	347
1993-01	203
1993-02	217
1993-03	227
1993-04	242
1993-05	185
1993-06	175
1993-07	252
1993-08	319
1993-09	202
1993-10	254
1993-11	336
1993-12	431
1994-01	150
1994-02	280
1994-03	187
1994-04	279
1994-05	193
1994-06	227
1994-07	225
1994-08	205
1994-09	259
1994-10	254
1994-11	275
1994-12	394
1995-01	159
1995-02	230
1995-03	188
1995-04	195
1995-05	189
1995-06	220
1995-07	274

Soft drink dataset

YearMonth	SoftDrinkProduction
1980-01	1954
1980-02	2302
1980-03	3054
1980-04	2414
1980-05	2226
1980-06	2725
1980-07	2589
1980-08	3470
1980-09	2400
1980-10	3180
1980-11	4009
1980-12	3924
1981-01	2072
1981-02	2434
1981-03	2956
1981-04	2828
1981-05	2687
1981-06	2629
1981-07	3150
1981-08	4119
1981-09	3030
1981-10	3055
1981-11	3821
1981-12	4001
1982-01	2529
1982-02	2472
1982-03	3134
1982-04	2789
1982-05	2758
1982-06	2993
1982-07	3282
1982-08	3437
1982-09	2804
1982-10	3076
1982-11	3782
1982-12	3889
1983-01	2271
1983-02	2452
1983-03	3084
1983-04	2522
1983-05	2769
1983-06	3438
1983-07	2839
1983-08	3746
1983-09	2632
1983-10	2851
1983-11	3871
1983-12	3618
1984-01	2389
1984-02	2344
1984-03	2678
1984-04	2492
1984-05	2858
1984-06	2246
1984-07	2800
1984-08	3869
1984-09	3007
1984-10	3023
1984-11	3907
1984-12	4209
1985-01	2353
1985-02	2570
1985-03	2903
1985-04	2910
1985-05	3782
1985-06	2759
1985-07	2931
1985-08	3641
1985-09	2794
1985-10	3070
1985-11	3576
1985-12	4106
1986-01	2452
1986-02	2206
1986-03	2488
1986-04	2416
1986-05	2534
1986-06	2521
1986-07	3093
1986-08	3903
1986-09	2907
1986-10	3025
1986-11	3812
1986-12	4209
1987-01	2138
1987-02	2419
1987-03	2622
1987-04	2912
1987-05	2708
1987-06	2798
1987-07	3254
1987-08	2895
1987-09	3263
1987-10	3736
1987-11	4077
1987-12	4097
1988-01	2175
1988-02	3138
1988-03	2823
1988-04	2498
1988-05	2822
1988-06	2738
1988-07	4137
1988-08	3515
1988-09	3785
1988-10	3632
1988-11	4504
1988-12	4451
1989-01	2550
1989-02	2867
1989-03	3458
1989-04	2961
1989-05	3163
1989-06	2880
1989-07	3331
1989-08	3062
1989-09	3534
1989-10	3622
1989-11	4464
1989-12	5411
1990-01	2564
1990-02	2820
1990-03	3508
1990-04	3088
1990-05	3299
1990-06	2939
1990-07	3320
1990-08	3418
1990-09	3604
1990-10	3495
1990-11	4163
1990-12	4882
1991-01	2211
1991-02	3260
1991-03	2992
1991-04	2425
1991-05	2707
1991-06	3244
1991-07	3965
1991-08	3315
1991-09	3333
1991-10	3583
1991-11	4021
1991-12	4904
1992-01	2252
1992-02	2952
1992-03	3573
1992-04	3048
1992-05	3059
1992-06	2731
1992-07	3563
1992-08	3092
1992-09	3478
1992-10	3478
1992-11	4308
1992-12	5029
1993-01	2075
1993-02	3264
1993-03	3308
1993-04	3688
1993-05	3136
1993-06	2824
1993-07	3644
1993-08	4694
1993-09	2914
1993-10	3686
1993-11	4358
1993-12	5587
1994-01	2265
1994-02	3685
1994-03	3754
1994-04	3708
1994-05	3210
1994-06	3517
1994-07	3905
1994-08	3670
1994-09	4221
1994-10	4404
1994-11	5086
1994-12	5725
1995-01	2367
1995-02	3819
1995-03	4067
1995-04	4022
1995-05	3937
1995-06	4365
1995-07	4290