Time series with RNN in TensorFlow - Model Pipeline Trace

Model Pipeline - Time series with RNN

This pipeline uses a Recurrent Neural Network (RNN) to learn patterns from time series data. It predicts future values based on past sequences.

Data Flow - 4 Stages

1Raw time series data

1000 time steps x 1 feature→Collect sequential data points over time→1000 time steps x 1 feature

[0.5, 0.6, 0.55, 0.7, 0.65, ...]

↓

2Windowing sequences

1000 time steps x 1 feature→Create input-output pairs by sliding window of size 10→990 samples x 10 time steps x 1 feature

Input: [[0.5,...0.6], ..., [0.65,...0.7]], Output: [0.65, 0.7, ...]

↓

3Train/test split

990 samples x 10 time steps x 1 feature→Split data into 80% train and 20% test sets→Train: 792 samples x 10 x 1, Test: 198 samples x 10 x 1

Train input shape: (792,10,1), Test input shape: (198,10,1)

↓

4RNN model training

792 samples x 10 time steps x 1 feature→Train RNN to predict next value from sequence→Model learns weights to map input sequences to next value

Model input shape: (None, 10, 1), output: scalar prediction

Training Trace - Epoch by Epoch

Loss
0.05 | *
0.04 |  *
0.03 |   *
0.02 |    *
0.01 |     **
      +------------
       1 5 10 15 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.045	N/A	Initial loss is moderate; model starts learning sequence patterns
5	0.020	N/A	Loss decreases steadily; model improves prediction accuracy
10	0.012	N/A	Loss continues to decrease; model converges well on training data
15	0.010	N/A	Loss stabilizes; model has learned useful temporal features

Prediction Trace - 3 Layers

Layer 1: Input sequence

Layer 2: RNN layer (SimpleRNN)

Layer 3: Dense output layer

Model Quiz - 3 Questions

What does the sliding window operation do in this pipeline?

A Splits data into training and testing sets

B Normalizes the data values

C Creates input-output pairs from sequences

D Reduces the number of features

Key Insight

RNNs learn from sequences by remembering past information step-by-step. This helps predict future values in time series by capturing temporal patterns.