  Analysis of time-series data allows to identify long-term trends and make
predictions that can help to improve our lives. With the rapid development of
artificial neural networks, long short-term memory (LSTM) recurrent neural
network (RNN) configuration is found to be capable in dealing with time-series
forecasting problems where data points are time-dependent and possess
seasonality trends. Gated structure of LSTM cell and flexibility in network
topology (one-to-many, many-to-one, etc.) allows to model systems with multiple
input variables and control several parameters such as the size of the
look-back window to make a prediction and number of time steps to be predicted.
These make LSTM attractive tool over conventional methods such as
autoregression models, the simple average, moving average, naive approach,
ARIMA, Holt's linear trend method, Holt's Winter seasonal method, and others.
In this paper, we propose a hardware implementation of LSTM network
architecture for time-series forecasting problem. All simulations were
performed using TSMC 0.18um CMOS technology and HP memristor model.
