Table 3 Performance analysis of EGMF-GR across datasets. Avg denotes the arithmetic mean over horizons 96, 192, 336, and 720 under the matched protocol. A dagger indicates statistical significance after Benjamini Hochberg correction based on paired Wilcoxon signed rank tests on rolling origin errors, comparing EGMF-GR with the corresponding backbone baseline under the same setting. For compactness, significance marks are reported for Avg MSE.

From: Hybrid evolutionary-gradient training improves long-term time series forecasting

Models

Our iTransformer

iTransformer

Our crossformer

Crossformer

Our TimesNet

TimesNet

Metric

MSE

MAE

MSE

MAE

MSE

MAE

MSE

MAE

MSE

MAE

MSE

MAE

ETTm1

96

0.328

0.367

0.334

0.368

0.347

0.401

0.404

0.426

0.332

0.369

0.338

0.375

192

0.374

0.388

0.377

0.391

0.398

0.428

0.450

0.451

0.377

0.392

0.374

0.387

336

0.408

0.411

0.426

0.420

0.551

0.557

0.532

0.515

0.430

0.426

0.410

0.411

720

0.474

0.447

0.491

0.459

0.765

0.674

0.666

0.589

0.516

0.467

0.478

0.450

Avg

0.396 \(^{\dagger }\)

0.403

0.407

0.410

0.515

0.515

0.513

0.495

0.414 \(^{\dagger }\)

0.414

0.415

0.406

ETTm2

96

0.177

0.262

0.180

0.264

0.237

0.329

0.287

0.366

0.179

0.260

0.187

0.267

192

0.241

0.301

0.250

0.309

0.388

0.443

0.414

0.492

0.252

0.305

0.249

0.309

336

0.304

0.342

0.311

0.348

0.979

0.722

0.597

0.542

0.311

0.342

0.321

0.351

720

0.407

0.399

0.412

0.407

3.305

1.212

1.730

1.042

0.417

0.403

0.408

0.403

Avg

0.282 \(^{\dagger }\)

0.326

0.288

0.332

1.227

0.677

0.757

0.611

0.290 \(^{\dagger }\)

0.328

0.291

0.332

ETTh1

96

0.386

0.400

0.386

0.405

0.388

0.409

0.423

0.448

0.414

0.428

0.384

0.402

192

0.440

0.431

0.441

0.436

0.485

0.486

0.471

0.474

0.461

0.454

0.436

0.429

336

0.483

0.454

0.487

0.458

0.606

0.575

0.570

0.546

0.488

0.472

0.491

0.469

720

0.497

0.483

0.503

0.491

0.832

0.711

0.653

0.621

0.525

0.506

0.521

0.500

Avg

0.452 \(^{\dagger }\)

0.442

0.454

0.447

0.578

0.545

0.529

0.522

0.472 \(^{\dagger }\)

0.465

0.473

0.450

ETTh2

96

0.291

0.341

0.297

0.349

0.633

0.584

0.745

0.584

0.319

0.361

0.340

0.374

192

0.382

0.397

0.380

0.400

0.740

0.606

0.877

0.656

0.404

0.409

0.402

0.414

336

0.425

0.433

0.428

0.432

1.814

1.062

1.043

0.731

0.423

0.432

0.452

0.452

720

0.430

0.446

0.427

0.445

3.751

1.636

1.104

0.763

0.455

0.467

0.462

0.468

Avg

0.382 \(^{\dagger }\)

0.404

0.383

0.407

1.735

0.972

0.942

0.684

0.400 \(^{\dagger }\)

0.417

0.414

0.427

Electricity

96

0.150

0.241

0.148

0.240

0.142

0.241

0.219

0.314

0.165

0.267

0.168

0.272

192

0.163

0.253

0.162

0.253

0.157

0.257

0.231

0.322

0.175

0.275

0.184

0.289

336

0.178

0.270

0.178

0.269

0.183

0.285

0.246

0.337

0.191

0.293

0.198

0.300

720

0.220

0.305

0.225

0.317

0.275

0.305

0.280

0.363

0.221

0.315

0.220

0.320

Avg

0.178

0.267

0.178

0.270

0.189

0.272

0.244

0.334

0.188 \(^{\dagger }\)

0.288

0.193

0.295

Exchange

96

0.095

0.216

0.086

0.206

0.267

0.383

0.256

0.367

0.097

0.223

0.107

0.234

192

0.194

0.314

0.177

0.299

0.499

0.532

0.470

0.509

0.223

0.342

0.226

0.344

336

0.346

0.429

0.331

0.417

0.853

0.705

1.268

0.883

0.406

0.470

0.367

0.448

720

0.876

0.707

0.847

0.691

1.446

0.977

1.767

1.068

0.964

0.746

0.964

0.746

Avg

0.378

0.417

0.360

0.403

0.791

0.649

0.940

0.707

0.423

0.445

0.416

0.443

Traffic

96

0.400

0.272

0.395

0.268

0.513

0.256

0.522

0.290

0.575

0.310

0.593

0.321

192

0.419

0.278

0.417

0.276

0.521

0.268

0.530

0.293

0.605

0.315

0.617

0.336

336

0.431

0.287

0.433

0.283

0.520

0.302

0.558

0.305

0.608

0.317

0.629

0.336

720

0.460

0.302

0.467

0.302

0.535

0.315

0.589

0.328

0.615

0.321

0.640

0.350

Avg

0.428

0.285

0.428

0.282

0.522

0.285

0.550

0.304

0.601 \(^{\dagger }\)

0.316

0.620

0.336

Weather

96

0.180

0.221

0.174

0.214

0.167

0.236

0.158

0.230

0.167

0.216

0.172

0.220

192

0.227

0.260

0.221

0.254

0.208

0.279

0.206

0.277

0.216

0.259

0.219

0.261

336

0.282

0.300

0.278

0.296

0.268

0.326

0.272

0.335

0.279

0.301

0.280

0.306

720

0.357

0.348

0.358

0.347

0.360

0.399

0.398

0.418

0.364

0.357

0.365

0.359

Avg

0.262

0.282

0.258

0.278

0.251

0.310

0.259

0.315

0.257 \(^{\dagger }\)

0.283

0.259

0.286