Question: Problem 1 Use the hw9-p1-p2.csv dataset for this problem. Does the time series have a trend? Does it have seasonality? Problem 2 Use the hw9-p1-p2.csv

Problem 1 Use the hw9-p1-p2.csv dataset for this problem. Does the time series

have a trend? Does it have seasonality?

Problem 2 Use the hw9-p1-p2.csv dataset for this problem. Calculate the

autocorrelation with lag = 3 using the method we discussed in the class.

Problem 3 Use the hw9-p3.csv dataset for this problem. Suppose that you forecast

the time series using the following formula:

S2 = Y1

St = * Yt-1 + (1 - ) * St-1, for t 3

Here, St is the forecast value at time t.

(1). Calculate the RMSE for = 0.1.

(2). Calculate the RMSE for = 0.5.

(3). Which one is better?

For Problem 1, use any tool and justify answer.

For Problem 2 and Problem 3, Use a spreadsheet program, such as Excel, for calculations not a data mining/analysis software or any other tool that calculates the autocorrelation and the RMSE.

hw9-p1-p2.csv

Y t
326 1
248 2
425 3
412 4
420 5
505 6
261 7
193 8
200 9
203 10
354 11
307 12
259 13
206 14
58 15
140 16
180 17
199 18
152 19
223 20
105 21
136 22
182 23
111 24
160 25
149 26
305 27
163 28
89 29
73 30
157 31
158 32
212 33
163 34
111 35
157 36
140 37
162 38
177 39
152 40
167 41
172 42
154 43
203 44
142 45
143 46
216 47
177 48
218 49
174 50
165 51
192 52
270 53
214 54
210 55
273 56
182 57
178 58
356 59
304 60
387 61
290 62
353 63
165 64
438 65
338 66
162 67
320 68
320 69
286 70
214 71
296 72
229 73
319 74
212 75
366 76
303 77
213 78
175 79
262 80
262 81
431 82
109 83
571 84
349 85
315 86
365 87
438 88
421 89
174 90
301 91
354 92
288 93
393 94
219 95
416 96
370 97
222 98
168 99
182 100
363 101
443 102
435 103
267 104
235 105
286 106
295 107
419 108
431 109
569 110
340 111
244 112
143 113
542 114
455 115
454 116
515 117
594 118
367 119
450 120
510 121
708 122
753 123
600 124
626 125
465 126
454 127
509 128
409 129
734 130
587 131
570 132
765 133
632 134
518 135
568 136
702 137
550 138
744 139
827 140
821 141
646 142
986 143
968 144
1128 145
1097 146
1216 147
1025 148
1137 149
1243 150
1488 151
1292 152
1139 153
725 154
923 155
1629 156
1761 157
2038 158
2200 159
1809 160
1184 161
2047 162
2495 163
2482 164
2674 165
2841 166
2076 167
1967 168
2263 169
2744 170
2532 171
2288 172
2991 173
2721 174
1785 175
2225 176
3224 177
4464 178
2914 179
2501 180
1166 181
2845 182
4613 183
6477 184
5192 185
5356 186
4747 187
2463 188
3627 189
5675 190
5130 191
5475 192
4968 193
4677 194
3572 195
3720 196
5450 197
4985 198
5632 199
3995 200
4162 201
3760 202
3293 203
4509 204
5655 205
7424 206
2973 207
4060 208
3659 209
6135 210
6887 211
8542 212
3110 213
4358 214
4178 215
6419 216
7136 217
7635 218
7110 219
5396 220
4239 221
4906 222
5278 223
5545 224
5074 225
5657 226
4283 227
3224 228
2567 229
3987 230
4821 231
4935 232
4330 233
3750 234
3477 235
2215 236
3022 237
4222 238
2781 239
3957 240
2546 241
2270 242
1963 243
2186 244
2602 245
2982 246
3378 247
3004 248
1276 249
1319 250
1920 251
2213 252
2228 253
1949 254
1820 255
1480 256
967 257
1322 258
1803 259
1679 260
1822 261
1316 262
1150 263
1114 264
1788 265
1928 266
1734 267
1516 268
1428 269
1248 270
980 271
1553 272
1410 273
1677 274
1495 275
1281 276
892 277
1006 278
1413 279
1589 280
1577 281
1548 282
1508 283
1004 284
1018 285
1640 286
1857 287
1887 288
1880 289
1678 290
1103 291
1476 292
1865 293
2274 294
2301 295
2362 296
1817 297
1464 298
1683 299
2252 300
2455 301
2160 302
2263 303
2912 304
1566 305
2292 306
1938 307
2184 308
2107 309
1831 310
1376 311
1401 312
2004 313
1884 314
1962 315
1694 316
1265 317
1236 318
968 319
1370 320
1431 321
1452 322
1386 323
1085 324
812 325
825 326
1392 327
1260 328
1179 329
1115 330
786 331
481 332
718 333
888 334
1004 335
881 336
795 337
655 338
446 339
472 340
626 341
616 342
337 343
657 344
494 345
281 346
359 347
425 348
538 349
400 350
405 351
287 352
188 353
195 354
382 355
215 356
253 357
203 358
146 359
179 360
100 361
241 362
177 363
216 364
126 365
89 366
100 367
116 368
85 369
121 370
113 371
33 372
44 373
55 374
56 375
82 376
71 377
91 378
41 379
48 380
33 381
68 382
78 383
60 384
56 385
31 386
41 387
63 388
93 389
76 390
79 391
269 392
61 393
99 394
169 395
322 396
191 397
208 398
236 399
269 400
717 401
366 402
457 403
477 404
586 405
1243 406
657 407
598 408
742 409
844 410
2054 411
883 412
962 413
1046 414
1111 415
2587 416
1109 417
1368 418
1228 419
1182 420
2996 421
1254 422
1452 423
1373 424
1459 425
3335 426
1290 427
1400 428
1793 429
1591 430
4081 431
1557 432
1796 433
1593 434
1703 435
5484 436
1362 437
2096 438
1726 439
4752 440
1453 441
2716 442
1999 443
2024 444
4364 445
1283 446
1821 447
1885 448
1784 449
4095 450
1380 451
1448 452
1486 453
1543 454
3283 455
1184 456
1492 457
1532 458
1583 459
4466 460
1471 461
1560 462
1512 463
2815 464
1888 465
1355 466
1267 467
1343 468
3173 469
1115 470
1274 471
1432 472
1245 473
3243 474
1066 475
1290 476
1586 477
1598 478
4039 479
1397 480
1745 481
3708 482
5248 483
1848 484
2650 485
3196 486
2581 487
6801 488
2616 489
2978 490
5058 491
5497 492
2915 493
4838 494
5170 495
5179 496
11199 497
3720 498
5403 499
5472 500
5007 501
11078 502
4039 503
5133 504
5883 505
6345 506
13717 507
5531 508
7817 509
9042 510
10040 511
12983 512
9228 513
15163 514
21137 515
21397 516

hw9-p3.csv

Y t
326 1
248 2
425 3
412 4
420 5
505 6
261 7
193 8
200 9
203 10
354 11
307 12
259 13
206 14
58 15
140 16
180 17
199 18
152 19
223 20
105 21
136 22
182 23
111 24
160 25
149 26
305 27
163 28
89 29
73 30
157 31
158 32
212 33
163 34
111 35
157 36
140 37
162 38
177 39
152 40
167 41
172 42
154 43
203 44
142 45
143 46
216 47
177 48
218 49
174 50
165 51
192 52
270 53
214 54
210 55
273 56
182 57
178 58
356 59
304 60
387 61
290 62
353 63
165 64
438 65
338 66
162 67
320 68
320 69
286 70
214 71
296 72
229 73
319 74
212 75
366 76
303 77
213 78
175 79
262 80
262 81
431 82
109 83
571 84
349 85
315 86
365 87
438 88
421 89
174 90
301 91
354 92
288 93
393 94
219 95
416 96
370 97
222 98
168 99
182 100

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