Recurrent Neural Networks (RNN)

Table of Contents

1. Time Series Data

1.1. So Far

  • Regression, Classification, Dimension Reduction,

  • Based on snapshot-type data

  • Sequence matters

  • What is a sequence?

    • sentence
    • medical signals
    • speech waveform
    • vibration measurement
  • Sequence Modeling

    • Most of the real-world data is time-series
    • There are important bits to be considered
      • Past events
      • Relationship between events
        • Causality
        • Credit assignment
  • Learning the structure and hierarchy

  • Use the past and present observations to predict the future

1.2. (Determinstic) Sequences and Difference Equations

We will focus on linear difference equations (LDE), a surprisingly rich topic both theoretically and practivally.

For example,

$$ y[0]=1,\quad y[1]=\frac{1}{2},\quad y[2]=\frac{1}{4},\quad \cdots $$

or by closed-form expression,

$$y[n]=\left(\frac{1}{2}\right)^n,\quad n≥0 $$

or with a difference equation and an initial condition,

$$y[n]=\frac{1}{2}y[n−1],\quad y[0]=1$$

High order homogeneous LDE

$$y[n]=\alpha_1 y[n−1] + \alpha_2 y [n−2] + \cdots + \alpha_k y[n-k]$$

1.3. (Stochastic) Time Series Analysis

1.3.1. Stationarity and Non-Stationary Series

  • A series is stationary if there is no systematic change in mean and variance over time

    • Example: radio static
  • A series is non-stationary if mean and variance change over time

    • Example: GDP, population, weather, etc.

1.3.2. Dealing with Non-Stationarity

Linear trends

Non-linear trends

  • For example, population may grow exponentially

Seasonal trends
  • Some series may exhibit seasonal trends

  • For example, weather pattern, employment, inflation, etc.

Combining Linear, Quadratic, and Seasonal Trends

  • Some data may have a combintation of trends

  • One solution is to apply repeated differencing to the series

  • For example, first remove seasonal trend. Then remove linear trend

  • Inspect model fit by examining residuals Q-Q plot

  • Anternatively, include both linear and cyclical trend terms into the model

\begin{align*} Y_t &= \beta_1 + \beta_2 Y_{t-1} \\ &+ \beta_3 t + \beta_4 t^{\beta_5} \\ &+ \beta_6 \sin \frac{2\pi}{s}t + \beta_7 \cos \frac{2\pi}{s}t \\ &+ u_t \end{align*}

1.4. Time-Series Data

(almost) all the data coming from manufacturing environment are time-series data

  • sensor data,
  • process times,
  • material measurement,
  • equipment maintenance history,
  • image data, etc.

Manufacturing application is about one of the following:

  • prediction of time-series values
  • anomaly detection on time-series data
  • classification of time-series values
  • metrology and inspection

1.4.1. Definition of time-series

$$x: T \rightarrow \mathbb{R}^n \;\; \text{where}\;\; T=\{\cdots, t_{-2},t_{-1},t_0,t_1,t_2, \cdots \}$$

Example: material measurements: when $n=3$

$$x(t) = \begin{bmatrix} \text{average thickness}(t)\\ \text{thickness variance}(t)\\ \text{resistivity}(t) \end{bmatrix} $$

1.4.2. Supervised and Unsupervised Learning for Time-series

For supervised learning, we define two time series

$$x: T \rightarrow \mathbb{R}^n \;\; \text{and} \;\; y: T \rightarrow \mathbb{R}^m$$

Supervised time-series learning:

$$ \begin{align*} \text{predict} \quad &y(t_k) \\ \text{given} \quad & x(t_k), x(t_{k-1}), \cdots \;\, \text{and} \;\, y(t_{k-1}), y(t_{k-2}), \cdots \end{align*} $$

Unsupervised time-series anomaly detection

  • Find time segment that is considerably differnt from the rest

$$ \begin{align*} \text{find} \quad & k^* \\ \text{such that} \quad & x(t_k) |_{k=k^*}^{k^*+s} \;\; \text{is significantly different from} \;\, x(t_k) |_{k=-\infty}^{\infty} \end{align*} $$

1.5. Markov Process

1.5.1. Sequential Processes

  • Most classifiers ignored the sequential aspects of data

  • Consider a system which can occupy one of $N$ discrete states or categories $q_t \in \{S_1,S_2,\cdots,S_N\}$

  • We are interested in stochastic systems, in which state evolution is random

  • Any joint distribution can be factored into a series of conditional distributions

$$p(q_0,q_1,\cdots,q_T ) = p(q_0) \; p(q_1 \mid q_0) \; p( q_2 \mid q_1 q_0 ) \; p( q_3 \mid q_2 q_1 q_0 ) \cdots$$

Amost impossible to compute !!

$$p(q_0,q_1,\cdots,q_T ) = p(q_0) \; p(q_1 \mid q_0) \; p( q_2 \mid q_1 ) \; p( q_3 \mid q_2 ) \cdots$$

Possible and tractable !!

1.5.2. Markov Process

  • (Assumption) for a Markov process, the next state depends only on the current state:

$$ p(q_{t+1} \mid q_t,\cdots,q_0) = p(q_{t+1} \mid q_t)$$

  • More clearly

$$ p(q_{t+1} = s_j \mid q_t = s_i) = p(q_{t+1} = s_j \mid q_t = s_i,\; \text{any earlier history})$$

  • Given current state, the past does not matter
  • The state captures all relevant information from the history
  • The state is a sufficient statistic of the future

1.5.3. State Transition Matrix

For a Markov state $s$ and successor state $s'$, the state transition probability is defined by

$$ P_{ss'} = P \left[S_{t+1} = s' \mid S_t = s \right] $$

State transition matrix $P$ defines transition probabilities from all states $s$ to all successor states $s'$.