NIPS2019 Amazon「think globally, act locally : a deep neural network approach to high-dimensional time series forecasting」
1. Think Globally, Act Locally: A Deep Neural Network
Approach to High-Dimensional Time Series
Forecasting
Rajat Sen *1, Hsiang-Fu Yu *1, and Inderjit Dhillon *2
*1 Amazon, *2 Amazon and UT Austin
さえない (@saeeeeru)
2. 1. Motivation
2. Time-series Forecasting
3. Problem Setting
4. Proposed Approach
a. Matrix Factorization
b. Temporal Convolutional Network
c. LeveledInit : Initialization
d. DeepGLO : training, prediction
5. Experiments
Agenda
2
3. 1. Motivation
2. Time-series Forecasting
3. Problem Setting
4. Proposed Approach
a. Matrix Factorization
b. Temporal Convolutional Network
c. LeveledInit : Initialization
d. DeepGLO : training, prediction
5. Experiments
Agenda
3
15. 4. DeepGLO
① Matrix Factorization
行列分解による特徴抽出
② Global TCN
MFで得られるXからGlobalに予測
③ Local TCN
過去のoriginalシーケンス、
Globalな予測、外部変数を共変量
として学習・最終的な予測値を算出
15
16. d. DeepGLO // training models
① 行列分解 X と F の更新
DeepGLO クラスの学習メソッド
(全体)
② Global TCNの更新
16 ③ Local TCNの更新
17. d. DeepGLO // training factors
17
DeepGLO クラスの学習メソッド
(Matrix Factorization)
18. d. DeepGLO // training Global TCN
18
DeepGLO クラスの学習メソッド
(時系列特徴 Xの予測モデル)
19. d. DeepGLO // training Local TCN
19
DeepGLO クラスの学習メソッド
(最終予測モデル)
20. 1. Motivation
2. Time-series Forecasting
3. Problem Setting
4. Proposed Approach
a. Matrix Factorization
b. Temporal Convolutional Network
c. LeveledInit : Initialization
d. DeepGLO : training, prediction
5. Experiments
Agenda
20