Locality-Based Visual Outlier Detection Algorithm for Time Series

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left">Name </td><td class="align_center"><svg height="14.1238pt" id="M251" style="vertical-align:-5.487901pt" version="1.1" viewbox="-0.0498162 -8.6359 60.0749 14.1238" width="60.0749pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M610 18C585 26 567 34 540 68C517 97 499 128 476 171C452 215 425 276 413 304C496 332 570 394 570 494C570 555 545 595 509 619S419 650 364 650H139L133 622C216 615 219 612 203 527L129 132C112 40 105 36 23 28L17 0H279L285 28C199 34 194 40 211 132L239 284H284C320 284 334 275 351 236C374 182 394 140 420 93C459 23 495 -1 592 -8H600L610 18ZM480 485C480 424 449 372 403 342C374 323 338 316 293 316H245L291 562C296 589 301 601 311 608S337 618 358 618C432 618 480 575 480 485Z" id="g113-83"></path></g><g transform="matrix(.0091,0,0,-0.0091,8.086,3.132)"><path d="M502 500C497 551 492 613 491 650H43V617C122 612 130 606 130 521V129C130 45 122 38 41 33V0H319V33C227 38 217 45 217 129V305H319C411 305 413 295 427 225H461V421H427C414 357 411 348 319 348H217V580C217 609 219 612 250 612H330C401 612 423 605 439 578C452 557 458 539 467 496L502 500Z" id="g58-68"></path></g><g transform="matrix(.0091,0,0,-0.0091,12.826,3.132)"><path d="M442 39L431 68C420 63 406 57 394 57C377 57 359 71 359 118V301C359 353 349 393 314 424C291 443 261 451 230 451C171 438 107 401 79 381C59 366 46 355 46 340C46 316 72 295 91 295C106 295 117 303 122 319C131 350 139 371 152 384C163 397 178 404 197 404C246 404 281 365 281 293V278C260 262 186 232 124 213C69 194 41 161 41 112C41 47 92 -12 163 -12C195 -12 243 25 283 51C288 35 293 22 308 7C319 -4 339 -12 354 -12L442 39ZM281 87C262 68 226 51 199 51C169 51 129 74 129 126C129 162 151 181 190 198C211 208 259 229 281 240V87Z" id="g58-95"></path></g><g transform="matrix(.0091,0,0,-0.0091,16.949,3.132)"><path d="M244 0V30C178 35 170 41 170 107V710C136 697 72 680 18 675V647C83 642 89 639 89 572V107C89 43 80 35 16 30V0H244Z" id="g58-106"></path></g><g transform="matrix(.0091,0,0,-0.0091,19.287,3.132)"><path d="M327 325C324 350 312 408 304 433C283 442 256 451 213 451C120 451 59 391 59 320C59 243 125 210 188 181C237 158 266 136 266 93C266 52 231 24 195 24C134 24 93 81 74 148L42 143C42 106 51 37 59 23C78 8 125 -12 177 -12C257 -12 345 35 345 127C345 198 292 234 216 264C171 283 131 305 131 348C131 387 156 416 195 416C243 416 277 378 297 317L327 325Z" id="g58-113"></path></g><g transform="matrix(.0091,0,0,-0.0091,22.782,3.132)"><path d="M384 109C349 76 310 59 268 59C197 59 123 113 120 245C240 248 367 259 384 262C403 266 407 275 407 296C407 376 338 451 254 451H253C201 451 148 425 105 378C65 334 39 272 39 203C39 88 112 -12 235 -12C266 -12 339 5 402 84L384 109ZM230 411C285 411 319 365 319 313C319 298 313 293 294 293C236 291 180 290 125 290C141 370 185 411 230 411Z" id="g58-99"></path></g><g transform="matrix(.0091,0,0,-0.0091,26.732,3.132)"><path d="M731 650H485V618C556 614 580 601 585 558C588 530 592 485 592 390V153H589L173 650H22V618C69 614 92 606 112 582C131 558 132 555 132 483V266C132 175 129 128 125 96C120 49 95 36 36 33V0H281V33C212 36 188 50 183 98C180 129 177 175 177 266V510H179L603 -9H638V390C638 483 640 530 644 561C649 602 674 615 731 618V650Z" id="g58-76"></path></g><g transform="matrix(.0091,0,0,-0.0091,33.383,3.132)"><path d="M384 109C349 76 310 59 268 59C197 59 123 113 120 245C240 248 367 259 384 262C403 266 407 275 407 296C407 376 338 451 254 451H253C201 451 148 425 105 378C65 334 39 272 39 203C39 88 112 -12 235 -12C266 -12 339 5 402 84L384 109ZM230 411C285 411 319 365 319 313C319 298 313 293 294 293C236 291 180 290 125 290C141 370 185 411 230 411Z" id="g58-99"></path></g><g transform="matrix(.0091,0,0,-0.0091,37.332,3.132)"><path d="M472 439C432 432 382 428 334 425C303 442 271 451 236 451H235C158 451 54 398 54 285C54 216 97 171 140 152C124 133 93 105 53 90C52 81 55 63 64 48C77 27 102 5 137 -6C115 -22 77 -53 57 -73C40 -88 32 -107 32 -130C32 -189 96 -253 208 -253C340 -253 461 -159 461 -56C461 44 378 63 314 63C281 63 246 62 207 62C163 62 145 80 145 99C145 114 160 130 175 141C191 138 210 136 226 136C314 136 405 188 405 294C405 329 393 359 376 382L433 378C448 387 469 415 477 430L472 439ZM226 417C282 417 320 364 320 288S281 172 236 171C180 171 142 224 142 300S182 417 226 417ZM248 -9C291 -9 319 -11 346 -22S390 -57 390 -90C390 -151 339 -199 246 -199C171 -199 114 -159 114 -106C114 -79 132 -53 159 -30C178 -15 200 -9 248 -9Z" id="g58-101"></path></g><g transform="matrix(.0091,0,0,-0.0091,41.691,3.132)"><path d="M442 39L431 68C420 63 406 57 394 57C377 57 359 71 359 118V301C359 353 349 393 314 424C291 443 261 451 230 451C171 438 107 401 79 381C59 366 46 355 46 340C46 316 72 295 91 295C106 295 117 303 122 319C131 350 139 371 152 384C163 397 178 404 197 404C246 404 281 365 281 293V278C260 262 186 232 124 213C69 194 41 161 41 112C41 47 92 -12 163 -12C195 -12 243 25 283 51C288 35 293 22 308 7C319 -4 339 -12 354 -12L442 39ZM281 87C262 68 226 51 199 51C169 51 129 74 129 126C129 162 151 181 190 198C211 208 259 229 281 240V87Z" id="g58-95"></path></g><g transform="matrix(.0091,0,0,-0.0091,45.612,3.132)"><path d="M303 37L293 66C280 59 256 49 231 49S173 63 173 143V396H281C294 404 298 428 287 439H173V575L158 577L92 509V439H46L17 408L22 396H92V107C92 27 128 -12 192 -12C203 -12 218 -9 237 2L303 37Z" id="g58-114"></path></g><g transform="matrix(.0091,0,0,-0.0091,48.506,3.132)"><path d="M138 535C169 535 191 560 191 589C191 620 168 644 139 644S85 620 85 589C85 560 110 535 138 535ZM257 0V30C192 35 185 43 185 109V453C142 435 94 422 40 414V386C101 378 104 372 104 311V109C104 41 97 35 33 30V0H257Z" id="g58-103"></path></g><g transform="matrix(.0091,0,0,-0.0091,50.945,3.132)"><path d="M487 439H304V409C361 402 367 391 351 344C328 280 297 190 262 110C235 178 192 284 168 353C152 396 153 403 208 409V439H-1V409C54 401 61 394 85 338C133 221 179 111 224 -10H255C312 139 363 260 398 336C426 394 432 401 487 409V439Z" id="g58-116"></path></g><g transform="matrix(.0091,0,0,-0.0091,55.304,3.132)"><path d="M384 109C349 76 310 59 268 59C197 59 123 113 120 245C240 248 367 259 384 262C403 266 407 275 407 296C407 376 338 451 254 451H253C201 451 148 425 105 378C65 334 39 272 39 203C39 88 112 -12 235 -12C266 -12 339 5 402 84L384 109ZM230 411C285 411 319 365 319 313C319 298 313 293 294 293C236 291 180 290 125 290C141 370 185 411 230 411Z" id="g58-99"></path></g></svg> (%)</td><td class="align_center"><svg height="11.9316pt" id="M252" style="vertical-align:-3.2957pt" version="1.1" viewbox="-0.0498162 -8.6359 56.677 11.9316" width="56.677pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M610 18C585 26 567 34 540 68C517 97 499 128 476 171C452 215 425 276 413 304C496 332 570 394 570 494C570 555 545 595 509 619S419 650 364 650H139L133 622C216 615 219 612 203 527L129 132C112 40 105 36 23 28L17 0H279L285 28C199 34 194 40 211 132L239 284H284C320 284 334 275 351 236C374 182 394 140 420 93C459 23 495 -1 592 -8H600L610 18ZM480 485C480 424 449 372 403 342C374 323 338 316 293 316H245L291 562C296 589 301 601 311 608S337 618 358 618C432 618 480 575 480 485Z" id="g113-83"></path></g><g transform="matrix(.0091,0,0,-0.0091,8.086,3.132)"><path d="M502 500C497 551 492 613 491 650H43V617C122 612 130 606 130 521V129C130 45 122 38 41 33V0H319V33C227 38 217 45 217 129V305H319C411 305 413 295 427 225H461V421H427C414 357 411 348 319 348H217V580C217 609 219 612 250 612H330C401 612 423 605 439 578C452 557 458 539 467 496L502 500Z" id="g58-68"></path></g><g transform="matrix(.0091,0,0,-0.0091,12.826,3.132)"><path d="M442 39L431 68C420 63 406 57 394 57C377 57 359 71 359 118V301C359 353 349 393 314 424C291 443 261 451 230 451C171 438 107 401 79 381C59 366 46 355 46 340C46 316 72 295 91 295C106 295 117 303 122 319C131 350 139 371 152 384C163 397 178 404 197 404C246 404 281 365 281 293V278C260 262 186 232 124 213C69 194 41 161 41 112C41 47 92 -12 163 -12C195 -12 243 25 283 51C288 35 293 22 308 7C319 -4 339 -12 354 -12L442 39ZM281 87C262 68 226 51 199 51C169 51 129 74 129 126C129 162 151 181 190 198C211 208 259 229 281 240V87Z" id="g58-95"></path></g><g transform="matrix(.0091,0,0,-0.0091,16.949,3.132)"><path d="M244 0V30C178 35 170 41 170 107V710C136 697 72 680 18 675V647C83 642 89 639 89 572V107C89 43 80 35 16 30V0H244Z" id="g58-106"></path></g><g transform="matrix(.0091,0,0,-0.0091,19.287,3.132)"><path d="M327 325C324 350 312 408 304 433C283 442 256 451 213 451C120 451 59 391 59 320C59 243 125 210 188 181C237 158 266 136 266 93C266 52 231 24 195 24C134 24 93 81 74 148L42 143C42 106 51 37 59 23C78 8 125 -12 177 -12C257 -12 345 35 345 127C345 198 292 234 216 264C171 283 131 305 131 348C131 387 156 416 195 416C243 416 277 378 297 317L327 325Z" id="g58-113"></path></g><g transform="matrix(.0091,0,0,-0.0091,22.782,3.132)"><path d="M384 109C349 76 310 59 268 59C197 59 123 113 120 245C240 248 367 259 384 262C403 266 407 275 407 296C407 376 338 451 254 451H253C201 451 148 425 105 378C65 334 39 272 39 203C39 88 112 -12 235 -12C266 -12 339 5 402 84L384 109ZM230 411C285 411 319 365 319 313C319 298 313 293 294 293C236 291 180 290 125 290C141 370 185 411 230 411Z" id="g58-99"></path></g><g transform="matrix(.0091,0,0,-0.0091,26.732,3.132)"><path d="M46 650V618C122 613 130 611 130 522V128C130 46 123 40 40 32V0H319V32C227 40 217 42 217 128V279L292 260C303 259 322 258 338 261C436 272 534 337 534 467C534 534 508 580 469 609C428 640 371 650 299 650H46ZM217 558C217 585 220 596 228 603C235 610 256 615 284 615C367 615 435 571 435 463C435 339 358 304 291 304C256 304 229 310 217 317V558Z" id="g58-78"></path></g><g transform="matrix(.0091,0,0,-0.0091,31.69,3.132)"><path d="M262 451C169 451 38 375 38 210C38 98 121 -12 261 -12C360 -12 481 64 481 227C481 351 386 451 262 451ZM249 414C337 414 385 320 385 204C385 70 333 25 272 25C189 25 134 117 134 241C134 355 189 414 249 414Z" id="g58-109"></path></g><g transform="matrix(.0091,0,0,-0.0091,36.413,3.132)"><path d="M327 325C324 350 312 408 304 433C283 442 256 451 213 451C120 451 59 391 59 320C59 243 125 210 188 181C237 158 266 136 266 93C266 52 231 24 195 24C134 24 93 81 74 148L42 143C42 106 51 37 59 23C78 8 125 -12 177 -12C257 -12 345 35 345 127C345 198 292 234 216 264C171 283 131 305 131 348C131 387 156 416 195 416C243 416 277 378 297 317L327 325Z" id="g58-113"></path></g><g transform="matrix(.0091,0,0,-0.0091,39.834,3.132)"><path d="M138 535C169 535 191 560 191 589C191 620 168 644 139 644S85 620 85 589C85 560 110 535 138 535ZM257 0V30C192 35 185 43 185 109V453C142 435 94 422 40 414V386C101 378 104 372 104 311V109C104 41 97 35 33 30V0H257Z" id="g58-103"></path></g><g transform="matrix(.0091,0,0,-0.0091,42.227,3.132)"><path d="M303 37L293 66C280 59 256 49 231 49S173 63 173 143V396H281C294 404 298 428 287 439H173V575L158 577L92 509V439H46L17 408L22 396H92V107C92 27 128 -12 192 -12C203 -12 218 -9 237 2L303 37Z" id="g58-114"></path></g><g transform="matrix(.0091,0,0,-0.0091,45.121,3.132)"><path d="M138 535C169 535 191 560 191 589C191 620 168 644 139 644S85 620 85 589C85 560 110 535 138 535ZM257 0V30C192 35 185 43 185 109V453C142 435 94 422 40 414V386C101 378 104 372 104 311V109C104 41 97 35 33 30V0H257Z" id="g58-103"></path></g><g transform="matrix(.0091,0,0,-0.0091,47.56,3.132)"><path d="M487 439H304V409C361 402 367 391 351 344C328 280 297 190 262 110C235 178 192 284 168 353C152 396 153 403 208 409V439H-1V409C54 401 61 394 85 338C133 221 179 111 224 -10H255C312 139 363 260 398 336C426 394 432 401 487 409V439Z" id="g58-116"></path></g><g transform="matrix(.0091,0,0,-0.0091,51.919,3.132)"><path d="M384 109C349 76 310 59 268 59C197 59 123 113 120 245C240 248 367 259 384 262C403 266 407 275 407 296C407 376 338 451 254 451H253C201 451 148 425 105 378C65 334 39 272 39 203C39 88 112 -12 235 -12C266 -12 339 5 402 84L384 109ZM230 411C285 411 319 365 319 313C319 298 313 293 294 293C236 291 180 290 125 290C141 370 185 411 230 411Z" id="g58-99"></path></g></svg> (%)</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">The feature space-based method</td><td class="align_center">100</td><td class="align_center">100</td></tr><tr><td class="align_left">The extended SAX-based method</td><td class="align_center">100</td><td class="align_center">100</td></tr><tr><td class="align_left">The proposed method</td><td class="align_center">0</td><td class="align_center">66.7</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

The experimental comparison using <svg height="12.4698pt" id="M250" style="vertical-align:-3.18147pt" version="1.1" viewbox="-0.0498162 -9.28833 25.7544 12.4698" width="25.7544pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M536 404C536 423 520 448 491 448C445 448 398 404 308 283L286 338C255 416 242 448 217 448C182 448 138 402 92 341L111 321C149 368 169 378 178 378C188 378 198 363 214 320L254 212C185 117 138 65 107 65C98 65 85 69 82 75C79 82 73 86 65 86C44 86 23 60 23 39C23 7 44 -12 71 -12C119 -12 168 33 265 177L306 61C321 17 347 -12 373 -12C413 -12 465 33 507 96L491 119C459 84 432 60 413 60C395 60 378 92 358 148L321 250C341 279 369 310 389 332C417 363 439 382 456 382C466 382 475 376 481 368C486 361 492 358 496 358C513 358 536 381 536 404Z" id="g113-121"></path></g><g transform="matrix(.0091,0,0,-0.0091,7.202,3.132)"><path d="M414 144C384 79 371 75 317 75H135L276 221C367 316 408 376 408 465C408 570 327 635 237 635C179 635 131 609 100 575L42 494L67 471C94 510 138 565 205 565C277 565 321 517 321 435C321 348 258 270 195 195C146 137 88 81 33 26V0H411C423 44 433 88 446 135L414 144Z" id="g50-51"></path></g><g transform="matrix(.013,0,0,-0.013,12.148,0)"><path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z" id="g113-41"></path></g><g transform="matrix(.013,0,0,-0.013,16.646,0)"><path d="M324 430H196L233 583L223 592L145 529L120 430H54L29 396L31 388H111L56 126C33 15 54 -12 77 -12C137 -12 214 57 250 95L233 119C208 92 155 59 138 59C126 59 120 70 131 125L186 390L298 394L324 430Z" id="g113-117"></path></g><g transform="matrix(.013,0,0,-0.013,21.08,0)"><path d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z" id="g113-42"></path></g></svg> of Ma_Data.

Security and Communication Networks

Locality-Based Visual Outlier Detection Algorithm for Time Series

Table 4